🧑 Human Understanding

📷 CVPR2026 · 218 paper notes

📌 Same area in other venues: 🧠 NeurIPS2026 (9) · 🎞️ ECCV2026 (171) · 🔬 ICLR2026 (55) · 🧪 ICML2026 (7) · 🤖 AAAI2026 (24) · 🧠 NeurIPS2025 (28)

🔥 Top topics: Avatars ×27 · Face & Gaze ×23 · Human Pose ×21 · Diffusion Models ×18 · Re-Identification ×13

SAM 3D Body: Robust Full-Body Human Mesh Recovery

SAM 3D Body (3DB) is a SAM-style promptable single-view full-body human mesh recovery model. It utilizes a shared encoder + body/hand dual-decoder architecture based on the MHR representation, which decouples skeleton and shape. Coupled with a data engine capable of mining hard samples and producing 7 million high-quality annotations, it achieves SOTA performance on both body and hand poses in in-the-wild images.

UniDex: A Robot Foundation Suite for Universal Dexterous Hand Control from Egocentric Human Videos

The authors propose the UniDex robot foundation suite—comprising a large-scale dataset across 8 dexterous hands (50K+ trajectories/9M frames), a Function-Actuator Aligned Space (FAAS), and a 3D VLA policy (UniDex-VLA). It achieves an 81% average task progress (vs. 38% for π₀) on real-world tool-use tasks and demonstrates spatial, object, and zero-shot cross-hand generalization capabilities.

Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation

The authors upgrade "talking head generation" from unidirectional broadcasting to genuine bidirectional conversation. By utilizing causal diffusion forcing in the motion latent space, the model receives user audio/motion while auto-regressively generating avatar head movements. Combined with KV caching, the latency is reduced to ~500ms (6.8x faster than baselines). Furthermore, a label-free DPO (Direct Preference Optimization), which generates negative samples by "dropping user conditions," enables the avatar to learn expressive reactions like nodding and smiling, achieving over an 80% preference rate against the strongest baseline in human evaluations.

AVATAR: Reinforcement Learning to See, Hear, and Reason Over Video

The AVATAR framework is proposed to improve GRPO through two core components: an off-policy training architecture (stratified replay buffer) and Time Advantage Shaping (TAS, using U-shaped weighting to emphasize the beginning and end of reasoning chains). This approach addresses three major issues of GRPO—data inefficiency, vanishing advantages, and uniform credit assignment—significantly outperforming the GRPO baseline on audio-visual reasoning benchmarks.

RAM: Recover Any 3D Human Motion in-the-Wild

RAM proposes a unified multi-person 3D motion recovery framework that integrates a motion-aware semantic tracker SegFollow (based on SAM2 + adaptive Kalman filtering), a memory-enhanced temporal human mesh recovery module T-HMR, a lightweight motion predictor, and a gated combiner. It achieves SOTA zero-shot tracking stability and 3D accuracy on benchmarks such as PoseTrack and 3DPW, with inference speeds 2-3 times faster than previous methods.

InterPrior: Scaling Generative Control for Physics-Based Human-Object Interactions

InterPrior utilizes a three-stage recipe of "large-scale imitation distillation + RL fine-tuning" to distill a full-reference imitation expert into a goal-conditioned variational policy. This policy is then refined using RL into a generalizable generative controller capable of generating full-body human-object interactions from sparse goals (snapshots/trajectories/contacts) and self-correcting after failures.

LLaMo: Scaling Pretrained Language Models for Unified Motion Understanding and Generation with Continuous Autoregressive Tokens

LLaMo extends pretrained LLMs into a unified large model capable of both "motion-to-text" (understanding) and "text-to-motion" (generation) using "modality-split Mixture-of-Transformers + continuous causal motion tokens + flow matching decoding heads + exit heads." The key is freezing text modules to preserve the original language capabilities of the LLM, while supporting real-time (≥30 FPS) streaming generation of arbitrary length.

M4Human: A Large-Scale Multimodal mmWave Radar Benchmark for Human Mesh Reconstruction

M4Human is the largest multimodal mmWave radar benchmark for Human Mesh Reconstruction (HMR) to date, featuring 661k frames, 50 actions, and 20 subjects. It provides synchronized RGB, Depth, Raw Radar Tensor (RT), and Radar Point Cloud (RPC) modalities with high-fidelity 3D mesh annotations based on optical motion capture (MoCap). It also introduces RT-Mesh, the first lightweight baseline for direct HMR from RT.

PHASE-Net: Physics-Grounded Harmonic Attention System for Efficient Remote Photoplethysmography Measurement

Starting from the Navier-Stokes equations, this work reveals through rigorous mathematical derivation that the rPPG pulse signal follows a second-order damped harmonic oscillator model. Its discrete solution is equivalent to a causal convolution operator, providing a first-principles justification for the choice of TCN architectures. The resulting PHASE-Net, with only 0.29M parameters, achieves SOTA performance across multiple datasets.

UniLS: End-to-End Audio-Driven Avatars for Unified Listening and Speaking

The first end-to-end unified speaking-listening facial expression generation framework, UniLS, is proposed. Through a two-stage training paradigm (learning intrinsic motion priors first, followed by dual-track audio fine-tuning), it simultaneously generates natural speaking and listening facial movements from dyadic audio inputs, achieving up to a 44.1% improvement in listening metrics.

FloodDiffusion: Tailored Diffusion Forcing for Streaming Motion Generation

FloodDiffusion tails the diffusion forcing framework from the video domain for text-driven streaming human motion generation. Through three key modifications—lower-triangular time scheduling, bi-directional attention within an active window, and frame-level time-varying text conditioning—it achieves a SOTA streaming FID of 0.057 on HumanML3D, approaching the performance of non-streaming methods for the first time.

FlexAvatar: Learning Complete 3D Head Avatars with Partial Supervision

FlexAvatar is proposed to resolve the entanglement between driving signals and target viewpoints by introducing learnable "bias sinks" tokens to unify training across monocular and multi-view data, enabling the generation of complete, high-quality, and animatable 3D head avatars from a single image.

FrankenMotion: Part-level Human Motion Generation and Composition

Addressing the limitation where text-to-human motion generation only allows sequence or action-level control but lacks control over individual body parts, this paper first utilizes an LLM agent (FrankenAgent) to automatically label existing mocap datasets into a three-level, temporally aligned fine-grained dataset named FrankenStein (Sequence / Atomic Action / Body Part). Subsequently, a diffusion-based model called FrankenMotion is trained, driven by per-frame text prompts for each body part, enabling the composition of complex motions not seen during training (e.g., "raising the left arm while sitting").

Geometric Neural Distance Fields for Learning Human Motion Priors

This paper proposes NRMF (Neural Riemannian Motion Fields), which models the third-order dynamics of human motion—"pose, velocity, and acceleration"—as the zero-level sets of three conditional neural distance fields. Equipped with a geometric projection algorithm and a geometric integrator, this single unconditional prior robustly handles tasks such as denoising, in-betweening, monocular fitting, and generation. It outperforms VAE and diffusion-based priors on benchmarks like AMASS, 3DPW, and PROX.

PC-Talk: Precise Facial Animation Control for Audio-Driven Talking Face Generation

PC-Talk performs "additive deformation" on the intermediate representation of implicit keypoints. It employs a LAC module to control lip-audio alignment with speaking styles and an EMC module to decouple pure emotional deformation by "subtracting neutral expressions." This enables fine-grained, controllable, real-time (30 FPS) talking face generation for speaking styles, lip-motion amplitude, emotional intensity, and even multi-region composite emotions, achieving SOTA on HDTF and MEAD.

ActAvatar: Temporally-Aware Precise Action Control for Talking Avatars

ActAvatar utilizes "structured text prompts + phase-aware cross-attention" to allow talking avatar videos to perform specific actions within designated time windows. Combined with "depth-progressive audio influence" and "two-stage training," it maintains lip-sync, action accuracy, and image quality without relying on pose skeletons, achieving 14B-level effects with a 5B model.

Causal Motion Diffusion Models for Autoregressive Motion Generation

The CMDM framework is proposed, which unifies diffusion denoising and autoregressive generation within a motion-language-aligned causal latent space. By employing frame-level independent noise levels and a causal uncertainty sampling schedule, it achieves high-quality, low-latency text-to-motion generation and long-sequence streaming synthesis.

DyaDiT: A Multi-Modal Diffusion Transformer for Socially Favorable Dyadic Gesture Generation

DyaDiT is a multi-modal Diffusion Transformer designed for dyadic dialogue scenarios. It employs an Orthogonalized Cross-Attention (ORCA) module to disentangle two channels of overlapping audio, integrates social conditions such as relationships/personality and a motion dictionary prior, and generates upper-body gestures that align with both dialogue dynamics and social context, surpassing existing dyadic gesture methods in objective metrics and user preference.

FusionAgent: A Multimodal Agent with Dynamic Model Selection for Human Recognition

This paper proposes FusionAgent, a Multimodal Large Language Model (MLLM) agent framework for dynamic sample-level model selection in whole-body biometric recognition. By encapsulating expert models (Face Recognition, Gait Recognition, Person Re-ID) as tools, the agent learns to adaptively select the optimal model combination for each test sample through Reinforcement Fine-Tuning (RFT). Combined with a novel ACT score fusion strategy, it significantly outperforms existing SOTA fusion methods.

RGB-Event based Pedestrian Attribute Recognition: A Benchmark Dataset and An Asymmetric RWKV Fusion Framework

This paper introduces the first RGB-Event multimodal pedestrian attribute recognition task and constructs EventPAR, the first large-scale dataset containing 100,000 paired RGB-Event frames with 6 types of emotional attributes. An asymmetric RWKV fusion framework (dual-stream RWKV encoding + OTN-RWKV event token filtering and bidirectional cross-fusion) is proposed, achieving SOTA performance across three datasets.

ViBES: A Conversational Agent with Behaviorally-Intelligent 3D Virtual Body

ViBES is proposed as a 3D conversational agent that unifies language, speech, and body movement. Through a Modality Mixture of Experts (MoME) architecture and cross-modal attention mechanisms, it generates temporally aligned facial expressions and full-body motions while preserving the conversational intelligence of pre-trained speech LLMs, shifting the paradigm beyond viewing behavior as simple "modality translation."

FMPose3D: monocular 3D pose estimation via flow matching

The authors reformulate monocular 2D-to-3D pose lifting as a "conditional distribution transport" problem. By utilizing Flow Matching to learn an ODE velocity field, the method transports Gaussian noise to valid 3D pose distributions in only 3 integration steps. A Reprojection Error-based Expectation Aggregation (RPEA) module merges multiple hypotheses into a single estimate. This approach outperforms diffusion-based methods on Human3.6M, MPI-INF-3DHP, and animal datasets while being approximately 5 times faster during inference.

Interact2Ar: Full-Body Human-Human Interaction Generation via Autoregressive Diffusion Models

Interact2Ar is the first text-conditioned, end-to-end autoregressive diffusion model that generates full-body two-person interactions with detailed hand movements. It uses a "collaborative denoiser + body/hand/trajectory-specific heads" to generate motion, and then coordinates the step-by-step generation of the entire sequence via a "mixed memory" autoregressive pipeline. This approach achieves state-of-the-art results on the Inter-X benchmark and unlocks key downstream capabilities like temporal stitching, perturbation adaptation, and multi-person interaction.

Miburi: Towards Expressive Interactive Gesture Synthesis

Miburi is proposed as the first online causal framework that directly utilizes the internal token streams of the speech-text foundation model Moshi and a 2D causal Transformer to achieve real-time synchronized full-body gesture and facial expression synthesis.

All in One: Unifying Deepfake Detection, Tampering Localization, and Source Tracing with a Robust Landmark-Identity Watermark

This paper proposes LIDMark, the first framework to unify deepfake detection, tampering localization, and source tracing into a single proactive forensics system. By embedding a 152-dimensional Landmark-Identity watermark (136D facial landmarks + 16D source ID), it utilizes intrinsic/extrinsic consistency to achieve three-in-one forensics, outperforming existing methods in both PSNR/SSIM and detection accuracy.

DecoVLN: Decoupling Observation, Reasoning, and Correction for Vision-and-Language Navigation

The DecoVLN framework is proposed to decouple the three processes of observation, reasoning, and error correction in VLN tasks. By utilizing an adaptive memory optimization mechanism and a state-action pair-based correction fine-tuning strategy, the framework achieves state-of-the-art (SOTA) performance on R2R-CE and RxR-CE using only egocentric RGB input.

FaceCoT: Chain-of-Thought Reasoning in MLLMs for Face Anti-Spoofing

This work constructs FaceCoT, the first large-scale VQA dataset for face anti-spoofing (FAS), containing 1.08 million samples across 14 attack types with six-level Chain-of-Thought (CoT) reasoning annotations (from global description to local reasoning to final conclusion). It proposes the CoT-Enhanced Progressive Learning (CEPL) two-stage training strategy, achieving an average AUC improvement of 4.06% and an HTER reduction of 5.00% across 11 benchmarks, surpassing all SOTA methods.

Goldilocks Test Sets for Face Verification

While mainstream face verification test sets have reached saturation (LFW 99.8%), this study does not rely on reducing image quality or adding occlusions to create difficulty. Instead, it extracts three types of "natural but difficult" image pairs from high-quality controlled face databases: extreme beard differences (Hadrian), strong exposure differences (Eclipse), and identical twins (ND-Twins). A set of "Goldilocks Three Rules" is proposed to ensure the test sets are "just right" in difficulty, resulting in difficulty levels that surpass artificial benchmarks relying on synthetic masks or reduced resolution.

InterAgent: Physics-based Multi-agent Command Execution via Diffusion on Interaction Graphs

InterAgent is the first text-driven, physics-based dual-humanoid agent control framework. It employs a multi-stream autoregressive diffusion Transformer (Inter-DiT) to decouple proprioception, exteroception, and action, and utilizes an "Interaction Graph + Sparse Edge Attention" to characterize fine-grained joint-to-joint relationships, generating physically plausible and semantically faithful dual-humanoid interactions from a single text command.

Mobile-VTON: High-Fidelity On-Device Virtual Try-On

The first fully offline mobile-side diffusion-based virtual try-on framework, based on the TeacherNet-GarmentNet-TryonNet (TGT) architecture. Through Feature-Guided Adversarial Distillation (FGA), the capabilities of SD3.5 Large are transferred to a lightweight student network with 415M parameters. It matches or even exceeds server-side baselines on VITON-HD and DressCode at 1024×768 resolution, with an end-to-end inference time of approximately 80 seconds (Xiaomi 17 Pro Max).

BIT: Matching-based Bi-directional Interaction Transformation Network for Visible-Infrared Person Re-Identification

Addressing the large modality gap and infrared sample scarcity in Visible-Infrared Person Re-Identification (VI-ReID), BIT discards the conventional approach of aligning features into a shared space. Instead, it adopts a matching-based paradigm: a bi-directional cross-interaction module allows visible-infrared image pairs to mutually absorb complementary information, followed by a query-aware scoring module that mines reliable reciprocal correspondences at the patch level to compute final similarity. BIT achieves SOTA results on SYSU-MM01, LLCM, and RegDB benchmarks.

DeX-Portrait: Disentangled and Expressive Portrait Animation via Explicit and Latent Motion Representations

By employing a hybrid motion representation—explicit global transformation for head pose and implicit latent code for facial expressions—alongside dual-branch pose injection and progressive blended CFG, this work achieves high-fidelity disentangled control of pose and expression in one-shot portrait animation for the first time, supporting fine-grained editing of pose or expression independently.

EgoPoseFormer v2: Accurate Egocentric Human Motion Estimation for AR/VR

Ours proposes EgoPoseFormer v2 (EPFv2), achieving SOTA accuracy in egocentric 3D human motion estimation (MPJPE 4.02cm, a 15-22% improvement over its predecessor) on the EgoBody3M benchmark with 0.8ms GPU latency. This is realized through an end-to-end Transformer architecture (Single Global Query + Causal Temporal Attention + Conditional Multi-view Cross-Attention) and an automatic labeling system based on uncertainty distillation.

Gaze Target Estimation Anywhere with Concepts

This paper introduces "Promptable Gaze Estimation (PGE)," a new task where a specific individual in a scene is designated via natural language or a coordinate, and the model directly produces a heatmap of their gaze location end-to-end. The authors provide the Gaze-Co dataset with 120K concept annotations and the first PGE model, GazeAnywhere, achieving SOTA across multiple benchmarks.

LCA: Large-scale Codec Avatars - The Unreasonable Effectiveness of Large-scale Avatar Pretraining

LCA applies the large-scale pre-training/post-training paradigm to 3D avatar modeling for the first time: pre-training on 1 million in-the-wild videos to learn broad appearance and geometric priors, followed by post-training on high-quality multi-view studio data to enhance fine expressions and fidelity, breaking the inherent trade-off between generalization and fidelity.

MotionHiFlow: Text-to-Motion via Hierarchical Flow Matching

MotionHiFlow decomposes text-to-3D human motion generation into a multi-stage flow matching process that is "coarse-to-fine and low-to-high temporal scale." It links flows across scales using a noise-consistent cross-scale transition. Combined with a dual-stream Text-Motion Diffusion Transformer (TMDiT) and joint-aware Joint RoPE, it achieves SOTA results on HumanML3D and KIT-ML (FID 0.032 / 0.135).

Next-Scale Autoregressive Models for Text-to-Motion Generation

MoScale proposes a next-scale autoregressive motion generation framework that replaces traditional next-token prediction. By employing coarse-to-fine hierarchical causal generation to capture global semantic structures, and introducing cross-scale hierarchical refinement alongside intra-scale temporal refinement, it achieves SOTA results on HumanML3D and KIT-ML (Top-1 0.540, FID 0.046).

SceMoS: Scene-Aware 3D Human Motion Synthesis by Planning with Geometry-Grounded Tokens

SceMoS replaces expensive 3D voxel/point cloud supervision with two lightweight 2D scene cues—DINOv2 features from Birds-Eye-View (BEV) for global semantic planning and local 2D height maps to embed surface physics directly into the motion token vocabulary. It achieves SOTA motion realism and contact accuracy on TRUMANS while reducing trainable scene encoding parameters by an order of magnitude (~4M vs. ~50M).

Seeing without Pixels: Perception from Camera Trajectories

This paper systematically promotes camera pose trajectories (6DoF pose sequences) as a standalone video perception modality for the first time. By training a lightweight Transformer encoder, CamFormer, through a contrastive learning framework, camera trajectories are mapped into a joint embedding space aligned with text. Experiments across 10 downstream tasks on 5 datasets demonstrate that camera trajectories are both lightweight and robust video signals—even outperforming video models with thousands of times higher computational costs in physical activities.

HandX: Scaling Bimanual Motion and Interaction Generation

HandX is constructed as a unified bimanual motion generation infrastructure (comprising 54.2 hours of motion data + 485,000 fine-grained text annotations). A decoupled automatic annotation strategy (kinematic feature extraction + LLM reasoning for description generation) is proposed. Diffusion and autoregressive generation paradigms are benchmarked, demonstrating clear data and model scaling trends.

LiveGesture: Streamable Co-Speech Gesture Generation Model

This paper proposes LiveGesture—the first fully streamable, zero look-ahead speech-driven full-body gesture generation framework. It employs a streaming vector quantized motion tokenizer (SVQ, featuring asymmetric bidirectional encoding + causal decoding) to discretize each body region into causal motion tokens. A Hierarchical Autoregressive Transformer (region experts xAR + causal spatio-temporal fusion xAR-Fuse) then generates SMPL-X full-body gestures frame-by-frame while receiving audio, achieving or exceeding offline SOTA performance on BEAT2 under strict streaming constraints.

MatchED: Crisp Edge Detection Using End-to-End, Matching-based Supervision

MatchED proposes a lightweight (approx. 21K parameters) plug-and-play module that generates crisp (single-pixel wide) edge maps through one-to-one bipartite matching based on spatial distance and confidence during training. It can be attached to any edge detector for end-to-end training and, for the first time, matches or exceeds standard post-processing methods without relying on NMS and thinning.

MoLingo: Motion-Language Alignment for Text-to-Human Motion Generation

MoLingo achieves overall SOTA performance in FID, R-Precision, and user studies for text-to-human motion generation. This is accomplished by performing masked autoregressive rectified flow on a continuous latent space, utilizing a Semantically Aligned Autoencoder (SAE) and multi-token cross-attention for text condition injection.

PolySLGen: Online Multimodal Speaking-Listening Reaction Generation in Polyadic Interaction

PolySLGen feeds past speech and motion of a multi-person group into a LoRA-fine-tuned LLM to online generate the target participant's future speech, body motion, and a "speaking state score." By unifying multi-person non-verbal signals with a Pose Fusion module and a Social Cue Encoder, it models both speaking and listening behaviors. It significantly outperforms baselines that naively extend dyadic methods to polyadic settings in terms of motion quality, speech-motion alignment, and speaking state prediction.

RefTon: Reference Person Shot Assist Virtual Try-on

This paper proposes RefTon, a human-to-human virtual try-on framework based on Flux-Kontext. It introduces additional reference images (photos of others wearing the target garment) to provide more accurate clothing details. Through a two-stage training strategy and a rescaled position indexing mechanism, it achieves end-to-end try-on without auxiliary conditions (e.g., DensePose, segmentation masks), reaching SOTA performance on VITON-HD and DressCode.

ReMoGen: Real-time Human Interaction-to-Reaction Generation via Modular Learning from Diverse Data

ReMoGen is proposed as a modular framework for real-time human interaction-to-reaction motion generation. It leverages a frozen general motion prior learned from large-scale single-person motion data, adapts to different interaction domains (human-human/human-scene) via independently trained Meta-Interaction modules, and introduces Frame-wise Segment Refinement to achieve low-latency online updates (0.047s/frame), outperforming SOTA on Inter-X and LINGO datasets.

Vision-Language Attribute Disentanglement and Reinforcement for Lifelong Person Re-Identification

VLADR proposes leveraging fine-grained attribute knowledge from Vision-Language Models (VLMs) to enhance lifelong person re-identification. Through a two-stage training process involving Multi-granular Textual Attribute Disentanglement (MTAD) and Intra-domain Cross-modal Attribute Reinforcement (ICAR), it explicitly models cross-domain shared human attributes to achieve efficient knowledge transfer and forgetting mitigation. It outperforms the Prev. SOTA by 1.9%-2.2% in anti-forgetting and 2.1%-2.5% in generalization.

4DSurf: High-Fidelity Dynamic Scene Surface Reconstruction

This paper proposes 4DSurf, a general dynamic scene surface reconstruction framework based on 2D Gaussian Splatting. By introducing Gaussian motion-induced SDF flow regularization to constrain the temporally consistent evolution of the surface and employing an overlapping segment strategy to handle large deformations, it surpasses existing SOTA methods with Chamfer distance improvements of 49% and 19% on the Hi4D and CMU Panoptic datasets, respectively.

CIGPose: Causal Intervention Graph Neural Network for Whole-Body Pose Estimation

Ours proposes CIGPose, a causal intervention graph pose estimation framework. It identifies visual context confounders through structural causal models, utilizes prediction uncertainty to locate keypoints affected by confounding, and replaces them with learned context-free canonical embeddings. A hierarchical GNN then models skeletal anatomical constraints, achieving a new SOTA of 67.0% AP on COCO-WholeBody.

COG: Confidence-aware Optimal Geometric Correspondence for Unsupervised Single-reference Novel Object Pose Estimation

The COG framework is proposed to model cross-view correspondences as a confidence-aware Optimal Transport (OT) problem. By predicting point-wise confidence as transport marginal constraints, it suppresses non-overlapping regions and outliers, achieving single-reference 6DoF pose estimation for novel objects under unsupervised conditions that is comparable to supervised methods.

CoordSpeaker: Exploiting Gesture Captioning for Coordinated Caption-Empowered Co-Speech Gesture Generation

CoordSpeaker first employs a "gesture captioning" framework to offline generate multi-granularity descriptive text for gesture data lacking textual annotations. It then utilizes a conditional latent diffusion model with a "hierarchical condition injection denoiser" to coordinate heterogeneous audio and text conditions. This generates full-body speaker gestures that are both rhythmically aligned with speech and responsive to textual instructions (e.g., "bowing while speaking").

Decoupled Generative Modeling for Human-Object Interaction Synthesis

DecHOI decomposes "Human-Object Interaction Synthesis" into two lightweight diffusion experts: a Trajectory Generator first plans global paths for the human and object without manual waypoints, followed by an Action Generator that completes fine-grained full-body actions conditioned on these paths. It utilizes an adversarial discriminator targeting end-joint contact dynamics to bridge the realism gap. DecHOI outperforms CHOIS/HOIFHLI on most metrics in FullBodyManipulation and 3D-FUTURE datasets and supports real-time replanning when encountering moving obstacles.

E-3DPSM: A State Machine for Event-Based Egocentric 3D Human Pose Estimation

E-3DPSM is proposed as an event-based state machine for egocentric 3D human pose estimation. It models pose estimation as a continuous-time state evolution process, utilizing bidirectional SSMs for temporal modeling and a learnable Kalman-style fusion module to integrate direct and incremental predictions. It achieves 80Hz real-time inference, reduces MPJPE by 19%, and improves temporal stability by 2.7 times.

EventGait: Towards Robust Gait Recognition with Event Streams

EventGait performs gait recognition using event cameras and proposes a dual-stream framework: a dynamic stream uses a Mixture of Spiking Experts (MoSE) with varying membrane time constants to adaptively capture multi-scale motion, while a static stream employs Cross-modal Structural Alignment (CroSA) with DINOv2 as a teacher to distill dense shape priors into sparse events. It matches camera-based methods in normal light and significantly outperforms them in low light (+37.3% at night) on both synthetic and real event gait benchmarks.

GazeShift: Unsupervised Gaze Estimation and Dataset for VR

Addressing the dilemma of "off-axis near-eye IR cameras + no reliable labels" in VR headsets, this work releases VRGaze (68 subjects, 2.1M frames), the first large-scale off-axis gaze dataset. It proposes GazeShift, which uses "gaze redirection between two frames of the same eye" as an unsupervised proxy task. By decoupling gaze and appearance via standard cross-attention and using the model's own attention maps as soft masks to focus on the eye region, the model achieves a 1.84° error on VRGaze with only 342K parameters and 55 MFLOPs (5ms inference on headset GPUs), approaching supervised performance.

InterPhys: Physics-aware Human Motion Synthesis in a Dynamic Scene

InterPhys proposes a differentiable continuous contact force model that integrates human-object, human-scene, and internal dynamics into the Euler-Lagrange equations. By employing a two-stage diffusion pipeline to first predict physical parameters and subsequently generate human motion, it significantly enhances the physical plausibility of human motion in dynamic scenes involving moving objects.

LaMoGen: Language to Motion Generation Through LLM-Guided Symbolic Inference

This work proposes the LabanLite symbolic motion representation and the LaMoGen framework, enabling LLMs to autonomously compose motion sequences through interpretable Laban symbolic reasoning for the first time, surpassing traditional text-motion joint embedding methods in temporal precision and controllability.

LAMP: Localization Aware Multi-camera People Tracking in Metric 3D World

LAMP leverages the known 6-DoF poses of a headset to lift 2D human keypoints detected across multiple cameras into a unified world-frame 3D ray cloud at an early stage. A spatio-temporal Transformer then fits human SMPL motion directly to this ray cloud. This "lift-then-fit" paradigm completely decouples the wearer's head motion from the observed person's motion, achieving SOTA performance on monocular benchmarks and significantly outperforming baselines in multi-camera egocentric scenarios.

MMGait: Towards Multi-Modal Gait Recognition

MMGait constructs the most comprehensive multi-modal gait recognition benchmark dataset to date (5 sensors, 12 modalities, 725 subjects, 334K sequences) and proposes a new task of Omni-modal Gait Recognition along with a unified baseline model, OmniGait.

Mocap-2-to-3: Multi-view Lifting for Monocular Motion Recovery with 2D Pretraining

Mocap-2-to-3 reformulates "recovering 3D motion from monocular 2D poses" as a multi-view synthesis problem: a single-view motion diffusion model is first pretrained on massive 2D data, followed by multi-view fine-tuning on limited 3D data. Combined with decoupled local pose/global displacement representations and ground pointmap constraints, it recovers full-body motion with metric absolute positions from monocular input, outperforming SOTA methods in both camera-space and world-coordinates on RICH/AIST++.

OpenDance: Multimodal Controllable 3D Dance Generation with Large-scale Internet Data

OpenDance introduces OpenDanceSet, a 100-hour large-scale 3D dance dataset across 14 genres with multimodal annotations (music, text, 2D keypoints, trajectories) derived from internet videos. Simultaneously, it proposes OpenDanceNet, a unified framework utilizing "decoupled tokenization + multimodal masked joint prediction + inference-time re-masking refinement" to achieve high-fidelity and finely controllable 3D dance generation driven by "music + arbitrary condition combinations."

OpenT2M: No-frill Motion Generation with Open-source, Large-scale, High-quality Data

The authors identified train/val set leakage in existing Text-to-Motion (T2M) benchmarks, where models overfit rather than generalize. They constructed OpenT2M, a million-scale, physically plausible, second-level annotated, long-horizon open-source motion dataset. Accompanied by MonoFrill—a "no-frills" autoregressive model using a 2D-PRQ tokenizer that treats motion as a "time × body-part" 2D image—the work improves zero-shot R@1 on de-leaked OOD benchmarks from approximately 0.07 to 0.24.

Pressure2Motion: Hierarchical Human Motion Reconstruction from Ground Pressure with Text Guidance

Reconstructs full-body 3D human motion from a sequence of ground pressure maps plus a single text prompt, without cameras or wearables. By injecting sparse and noisy pressure signals into a pre-trained motion diffusion model via "dual-level pressure features + hierarchical pressure-modulated diffusion," it achieves state-of-the-art (SOTA) performance on this novel task using a self-built MPL benchmark.

rPPG-VQA: A Video Quality Assessment Framework for Unsupervised rPPG Training

rPPG-VQA proposes the first video quality assessment framework specifically for remote heart rate detection (rPPG), combining signal-level multi-method consensus SNR with scene-level MLLM interference identification, alongside a two-stage adaptive sampling strategy to filter in-the-wild videos for training set construction.

Text-guided Feature Disentanglement for Cross-modal Gait Recognition

This work generates a "modality+view"-aware gait text dictionary using LLMs and leverages CLIP to use text as semantic anchors for guiding visual feature disentanglement. It decomposes gait features from LiDAR and camera modalities into "modality-specific" and "modality-shared" components, performing retrieval using only the shared features. This approach achieves new SOTA results on the SUSTech1K and FreeGait benchmarks (e.g., FreeGait 3D→2D Rank-1 increases from 43.3 to 57.9).

View-Aware Semantic Alignment for Aerial-Ground Person Re-Identification

To address the drastic viewpoint differences between UAVs and ground cameras in Aerial-Ground Person Re-Identification (AGPReID), this paper proposes ViSA. Instead of pursuing forced "view-invariant" alignment of shared parts, it utilizes a set of Expert-driven Token Generation Modules (ETGM) to generate adaptive semantic queries. These queries are then anchored to their responsive local regions using a Dual-branch Local Fusion Module (DLFM) through graph reasoning. This simultaneously preserves view-invariant and view-specific identity cues, achieving a \(10.06\%\) mAP improvement on the CARGO cross-view protocol.

WildCap: Facial Albedo Capture in the Wild via Hybrid Inverse Rendering

WildCap is proposed as a hybrid inverse rendering framework (data-driven SwitchLight delighting + model-based texel grid lighting optimization + diffusion prior sampling). It reconstructs high-quality 4K facial diffuse albedo maps from smartphone in-the-wild videos, significantly narrowing the quality gap between uncontrolled capture and professional light stage methods.

Action Motifs: Self-Supervised Hierarchical Representation of Human Body Movements

This paper proposes A4Mer, a nested latent-space Transformer, which learns a two-level hierarchical representation of "Action Atoms → Action Motifs" from 3D pose sequences in a fully self-supervised manner. This allows semantically meaningful, reusable, variable-length action segments to "emerge" bottom-up, significantly outperforming existing fixed-granularity representations on action recognition, long-term motion prediction, and motion interpolation.

BarbieGait: An Identity-Consistent Synthetic Human Dataset with Versatile Cloth-Changing for Gait Recognition

Addressing the pain point that real-world collection of gait data for "one person wearing hundreds of outfits" is nearly impossible, this paper maps 521 real subjects into a virtual engine. By randomly generating 100 outfits per person, the authors construct an identity-consistent synthetic gait dataset, BarbieGait. A companion clothing-invariant baseline, GaitCLIF, is proposed, achieving SOTA results on BarbieGait and real-world datasets including CCPG, SUSTech1K, Gait3D, and GREW.

Beyond Scanpaths: Graph-Based Gaze Simulation in Dynamic Scenes

The authors model driver gaze as an autoregressive dynamical system: each frame of the traffic scene is encoded into a "gaze-centric" heterogeneous spatio-temporal graph. An Affinity Relational Transformer (ART) models the interaction between the gaze and traffic objects, while an Object-level Density Network (ODN) predicts the next-step gaze distribution, which is autoregressively unrolled into continuous gaze trajectories. This unified model simultaneously generates SOTA-level gaze time series, scanpaths, and saliency maps.

Differentially Private 2D Human Pose Estimation

The first unified differential privacy framework for 2D human pose estimation: it combines two denoising mechanisms, "Gradient Subspace Projection" and "Feature-level Differential Privacy (adding noise only to private features of the raw image)," into Feature-Projective DP. Under formal privacy guarantees, it significantly narrows the accuracy gap with non-private models (achieving 82.61% [email protected] on MPII at \(\varepsilon=0.8\), recovering 73% of the privacy-induced loss).

FlowPalm: Optical Flow Driven Non-Rigid Deformation for Geometrically Diverse Palmprint Generation

FlowPalm utilizes RAFT optical flow to statistically derive non-rigid deformation fields from real palmprint pairs, constructing a "deformation library." During diffusion sampling, these deformations are injected into the pipeline via three stages: crease warping for the backbone and warped noise for the texture. This produces geometrically diverse and identity-consistent synthetic palmprints. Notably, a recognition model trained solely on synthetic data (85.20% TAR) outperforms one trained on real data (73.59%).

Gaussian-Mixture Latent Flow for Stochastic 3D Human Motion Prediction

To address the long-standing issues in stochastic human motion prediction—sacrificing plausibility for accuracy and diversity and the inability to reliably quantify uncertainty—this paper learns a data-driven Gaussian Mixture Prior via EM in the latent space to decouple different motion modes. It then employs a fully invertible Latent Flow Matching (integrated with a skeleton-aware Transformer) for prediction. This approach enables both precise log-likelihood for uncertainty measurement and SOTA accuracy and plausibility on Human3.6M and AMASS.

GazeOnce360: Fisheye-Based 360° Multi-Person Gaze Estimation with Global-Local Feature Fusion

Using a single upward-facing desktop fisheye camera to capture a 360° scene, GazeOnce360 employs rotational convolutions, eye keypoint supervision, and global-local dual-resolution cross-attention to simultaneously detect and regress 3D gaze directions for multiple people in an end-to-end manner. On the self-built synthetic dataset MPSGaze360, it reduces gaze error from 18.96° (multi-stage pipeline) to 10.39° while achieving a ~4x speedup.

Ground Reaction Inertial Poser: Physics-based Human Motion Capture from Sparse IMUs and Insole Pressure Sensors

GRIP utilizes IMU signals and plantar pressure from 4 wearable devices (dual wrist smartwatches + dual foot smart insoles). It first estimates the kinematic state using KinematicsNet, and then employs DynamicsNet inside a physics simulator to drive a "digital twin" humanoid via torque using a PPO policy to replicate the motion. This approach reconstructs full-body motion with accurate global trajectories and physical consistency (no foot sliding, no ground penetration, no floating) under extremely sparse sensor configurations.

MOFA-VTON: More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-On

MOFA-VTON enables users to control "how to style tops and bottoms" (e.g., tucked in, tucked out, or various hemline styles) using a single hand-drawn curve sketch. It converts the sketch into a "dual-region mask" for layout guidance and utilizes "Layout Adaptation blocks" to spatially align upper and lower body features at the feature level. It achieves SOTA image quality on VITON-HD and DressCode while unlocking styling diversities unattainable by traditional methods.

MV-Fashion: Towards Enabling Virtual Try-On and Size Estimation with Multi-View Paired Data

MV-Fashion utilizes an "economical" multi-view synchronous capture rig consisting of 60 Raspberry Pi RGB cameras and 8 RGB-D cameras to record 3,273 synchronous videos (72.5M frames) of 80 subjects wearing 474 outfits (754 items). It provides multi-modal annotations for each garment, including flat-lay catalogue ↔ in-the-wild worn pairs, pixel-level segmentation, SMPL-X, point clouds, size charts, fabric elasticity, and styling. This marks the first time data required for virtual try-on, size estimation, and novel view synthesis are integrated into a single dataset, with baselines provided for all three tasks.

OMG-Bench: A New Challenging Benchmark for Skeleton-based Online Micro Hand Gesture Recognition

This paper constructs the first large-scale public benchmark for skeleton-based online micro hand gesture recognition, OMG-Bench (40 classes, 13,948 instances), and proposes the HMATr framework. By utilizing hierarchical memory banks and position-aware queries, HMATr achieves end-to-end unification of detection and classification, outperforming SOTA methods by 7.6% in detection rate.

OpenFS: Multi-Hand-Capable Fingerspelling Recognition with Implicit Signing-Hand Detection and Frame-Wise Letter-Conditioned Synthesis

The OpenFS framework is proposed, achieving multi-hand fingerspelling recognition via dual-level positional encoding + signing-hand focus loss + monotonic alignment loss for implicit signing-hand detection. It also designs a frame-wise letter-conditioned diffusion generator to synthesize OOV data, achieving SOTA on ChicagoFSWild/ChicagoFSWildPlus/FSNeo benchmarks with inference speeds over 100x faster than PoseNet.

ParTY: Part-Guidance for Expressive Text-to-Motion Synthesis

The ParTY framework is proposed to significantly improve the precision of text-motion semantic alignment for individual body parts while maintaining full-body motion coherence through a Part-Guided Network and Part-aware Text Grounding (PTG). It resolves the fundamental contradiction between "part expressiveness vs. full-body coherence" found in existing holistic and part-based methods.

Prompt-Anchored Vision–Text Distillation for Lifelong Person Re-identification

PAD treats the frozen CLIP text encoder as a cross-domain invariant "semantic anchor." By employing an asymmetric vision-text distillation—weak text-side distillation to ensure semantic stability and strong vision-side EMA distillation to maintain plasticity—it simultaneously suppresses catastrophic forgetting and semantic drift in exemplar-free lifelong person Re-ID. It achieves an average mAP of 70.7 on seen domains and 78.6 on unseen domains, significantly outperforming previous SOTA methods.

RoMo: A Large-Scale, Richly Organized Dataset and Semantic Taxonomy for Human Motion Generation

Addressing the long-standing dilemma in 3D human motion generation between "small but clean mocap sets" and "large but noisy in-the-wild sets," RoMo employs a taxonomy-aware adaptive filtering pipeline to distill approximately 1% of high-quality motion from 125,000 hours of web videos. It constructs a large-scale dataset featuring 820,000 segments (approx. 1,238 hours), each with 5 rich text descriptions organized by a three-level taxonomy (Category → Subcategory → Atomic action). Accompanied by the Motion Toolbox for unified evaluation, models trained on RoMo achieve SOTA performance in fidelity, diversity, and fine-grained textual understanding.

See Through the Noise: Improving Domain Generalization in Gaze Estimation

SeeTN attributes "poor cross-domain generalization in gaze estimation" to source domain label noise for the first time. It identifies noisy samples by aligning feature affinities with continuous label affinities via a prototype-constructed semantic manifold. By applying specific regularization to clean and noisy samples, it transfers supervision from clean to noisy samples, reducing angular error by 12–18% across four cross-domain settings without sacrificing source domain accuracy.

Sketch2Colab: Sketch-Conditioned Multi-Human Animation via Controllable Flow Distillation

Ours proposes Sketch2Colab, which generates coordinated multi-human-object interaction 3D motions from storyboard sketches by distilling sketch-driven diffusion priors into a rectified flow student network, combined with energy guidance and Continuous-Time Markov Chain (CTMC) discrete event planning, achieving SOTA constraint compliance and perceptual quality on CORE4D and InterHuman.

Stake the Points: Structure-Faithful Instance Unlearning

Structguard is proposed to maintain the semantic relationship structure between retained instances during the unlearning process through semantic anchors, preventing structural collapse. It achieves average improvements of 32.9%, 19.3%, and 22.5% in image classification, face recognition, and retrieval tasks, respectively.

Superman: Unifying Skeleton and Vision for Human Motion Perception and Generation

Superman unifies "3D pose perception from video" and "skeleton-based motion generation" into a conditional sequence generation problem: it first employs a vision-guided motion tokenizer (VQ-VAE + dual vision/geometry streams + hybrid codebook) to quantize continuous motion into cross-modal discrete tokens, then utilizes a single MLLM (Qwen2.5-VL-7B) to autoregressively predict these tokens. This framework performs 3D pose estimation, motion prediction, and motion in-betweening within a single model, achieving an 11~12% improvement over specialized SOTAs on Human3.6M.

Talking Together: Synthesizing Co-Located 3D Conversations from Audio

This paper introduces the first method to generate complete facial animations for two participants co-located in the same 3D space from a single mixed audio stream. By utilizing a dual-stream diffusion architecture (shared U-Net + cross-attention), a two-stage hybrid data training strategy, LLM-driven text-to-spatial layout control, and an auxiliary gaze loss, the system achieves natural mutual gaze, head movements, and space-aware 3D animation synthesis for dyadic conversations.

Translating Signals to Languages for sEMG-Based Activity Recognition

This paper proposes LLM-sEMG, which uses an sEMG-specific VQ-VAE to discretize continuous electromyography signals into tokens. Through a combination of "Lewis signaling games + human language inductive bias," these tokens evolve into a natural-language-like "sEMG language." Finally, a frozen pre-trained LLM with LoRA fine-tuning directly interprets this language for activity recognition, achieving accuracies of 95.14% on GRABMyo and 93.17% on NinaPro DB2, outperforming the strongest baseline STET by approximately 4 percentage points.

TriLite: Efficient WSOL with Universal Visual Features and Tri-Region Disentanglement

Using only a frozen DINOv2 ViT and a TriHead module with fewer than 800K trainable parameters, this method achieves new SOTA results in WSOL. It achieves this by disentangling patch features into foreground, background, and ambiguous regions and introducing an adversarial background loss.

Unified Number-Free Text-to-Motion Generation Via Flow Matching

UMF bridges single-person and multi-person motion datasets using a unified multi-token latent space. It establishes a "1+N" paradigm consisting of a "Pyramid Motion Flow (P-Flow)" for single-pass generation of motion priors and a "Semi-Noisy Motion Flow (S-Flow)" for iterative autoregressive generation of responses. This reaches SOTA on text-driven "number-free" multi-person generation (InterHuman FID 4.772) while performing inference approximately 5x faster than FreeMotion.

Unleashing Vision-Language Semantics for Deepfake Video Detection

This paper proposes VLAForge, which independently learns diverse forgery cues and localization maps through ForgePerceiver and integrates an identity-aware Vision-Language Alignment (VLA) scoring mechanism. By unleashing the potential of cross-modal semantics from Vision-Language Models (VLMs) to enhance discriminative capabilities, the method consistently outperforms existing SOTA methods across nine datasets.

AudioAvatar: Personalized Audio-driven Whole-body Talking Avatars

AudioAvatar reconstructs a canonical 3D Gaussian whole-body digital human from a single portrait and allows audio to directly modulate the motion trajectory of each Gaussian particle (skipping the lossy intermediate chain of "audio → parametric pose → rendering"). By leveraging large-scale audio-driven video diffusion models for feature distillation, it significantly outperforms pose-driven baselines in lip synchronization, facial micro-expressions, and gesture naturalness.

Beyond Single-View Sufficiency: CVBench for Cross-View Human Understanding

Addressing the loophole in existing MLLM benchmarks that default to "single-view sufficiency" and only reward single-image recognition, this work constructs CVBench—3,000 human understanding questions where each item is verifiably "unsolvable via single-view, solvable via cross-view" (12 spatio-temporal tasks, 4-way synchronized cameras). Evaluation reveals that even the strongest models lag nearly 50 points behind humans, identifying a systematic failure mechanism across all models: "single-view bias."

BoostSLT: Boosting Sign Language Translation via a Plug-and-Play Diffusion-Based Semantic Enhancer

BoostSLT introduces a plug-and-play module that wraps any sign language translation model. It segments long videos into semantic segments based on motion energy, translates segments independently, and reconstructs fragmented translations into coherent long sentences using a Diffusion Language Model. Without relying on gloss annotations, it significantly improves BLEU and ROUGE for long-sentence and document-level translation.

Breaking Spurious Correlations: Uncertainty-Driven Causal Transformers for AU Detection

Addressing the issues of data scarcity, class imbalance, label noise, and confounding bias in Facial Action Unit (AU) detection, this paper proposes the UDCT framework: it models Transformer attention weights as Gaussian distributions to explicitly represent uncertainty, uses this uncertainty to reweight sample losses against noise/imbalance, and employs per-AU causal backdoor adjustment to sever spurious AU correlations caused by confounders. Ours achieves competitive and more robust results on BP4D / DISFA (average F1 of 67.36% on DISFA).

Bridging Facial Understanding and Animation via Language Models

This paper leverages text-to-video (T2V) large models to synthesize an approximately 80-hour, emotion-balanced 3D facial corpus named Open3DFaceVid. By discretizing the facial geometry of each frame into "geometric tokens" via a VQ-VAE and feeding them to an LLM, this work, for the first time, models 3D facial parameters as a "language problem." A single, unified LLM can both translate facial motion tokens into natural language descriptions (Motion2Language) and generate controllable 3D facial motion trajectories from textual prompts (Language2Motion).

CLEX: Complementary Label Exchange Learning for Noisy Facial Expression Recognition

CLEX suppresses spurious activations by randomly exchanging a subset of non-target logits between a primary and an augmented branch, followed by scale-invariant normalization. It then employs a "complementary suppression loss" to specifically suppress responses of randomly retained non-target classes. Without requiring clean data or noise priors, CLEX achieves SOTA performance across various noise rates on three in-the-wild FER datasets: RAF-DB, AffectNet, and FERPlus.

Clothe and Pose

This paper integrates "virtual try-on" and "pose transfer" (originally decoupled into two sequential pipeline stages) into a single unified task (Clothe and Pose). Implementing an SDXL-based multi-stream diffusion model, the approach simultaneously processes the user image, front/back garment views, and target pose skeletons to generate the "designated person wearing designated clothes in a designated pose" in a single step. Furthermore, an evaluation protocol utilizing ground-truth triplets is introduced. The method comprehensively outperforms the "try-on + reposing" sequential baselines and the 20B Qwen-Image-Edit across four pose transformation configurations.

Composite-Attribute Person Re-Identification via Pose-Guided Disentanglement

Aiming at the natural but ambiguous query of "reference image + short keyword attributes," this paper proposes the CA-ReID task. It utilizes pose-guided "Part-Aware Representation (PAR)" to bind textual attributes to corresponding body regions and employs "Dense Disentanglement Loss (DDL)" to separate identity and attribute dimensions. This approach improves Recall@1 for Hard queries by up to +17% on the self-built composite attribute benchmark.

COPE: Consistent Occlusion and Prompt Enhancement Network for Occluded Person Re-identification

COPE addresses the deep-seated issues of "feature interference" and "information loss" in occluded ReID using three lightweight modules: Cross-Identity Consistent Occlusion (CICO) imposes the same occlusion across different identities and constrains feature consistency in occluded areas; Prompt-Based Background Filling (PBF) uses CLIP text prompts to locate the foreground and randomly fill the background; and Prompt Similarity Scoring (PSS) post-processes retrieval during inference based on foreground completeness scores. It achieves approximately 82% Rank-1 and 75–76% mAP on Occluded-Duke with almost no additional inference cost.

Dynamic Label Noise Suppression with Optimal Teacher Pool for Facial Expression Recognition

To address the prevalent label noise in Facial Expression Recognition (FER) datasets, this paper proposes the OTP-NS framework. It replaces a single EMA teacher with an "Optimal Teacher Pool" to break parameter coupling and noise accumulation between the teacher and student. Additionally, two sample-level denoising components, Similarity-Aware Label Smoothing (SALS) and Centroid Confidence Weighting (CWL), are integrated. The method outperforms existing SOTA across various noise ratios on multiple benchmarks with zero additional inference overhead.

Dynamic Magic: Unleashing Restricted Knowledge for Lifelong Person Re-Identification

To address the issue in Lifelong Person Re-Identification (LReID) where fixed network architectures cannot accommodate continuously accumulating knowledge, leading to catastrophic forgetting, this paper proposes the dynamic expansion framework VIA. It models each new domain independently using cascaded dual LoRA adapters, reuses cross-domain commonalities via a shared expert pool with routing, and adaptively adjusts encoder learning rates based on domain similarity. Ultimately, it improves the average mAP across 5 seen domains from 66.4% of the baseline to 77.7%.

D³FER: Dual Channel and Dual Branch Network for Robust Facial Expression Recognition under Dual Challenges

Aiming at the compound challenge of "visual disturbances (occlusion/pose) + label noise" in in-the-wild facial expression recognition (FER), D³FER feeds weak/strong dual-channel augmentations into a Query-Key momentum dual-branch network. It utilizes a cross-batch dynamic queue to cache both confidence scores for adaptive threshold-based sample filtering and label correction, as well as features for supervised contrastive learning. During inference, the smoother Key branch is used, achieving new SOTA results on RAF-DB/FERPlus/AffectNet and their occlusion/pose/noise subsets.

FisherPoser: Human Motion Estimation from Sparse Observations with Hierarchical Region-Wise Fisher-Matrix Uncertainty Modeling

FisherPoser models the estimation of full-body poses from three 6-DoF signals (HMD + two controllers) as probabilistic inference on the \(SO(3)\) manifold. Instead of a single rotation, each joint outputs a Matrix-Fisher distribution. By utilizing "five-region tokens + parent-to-child recursion along kinematic chains," both pose and uncertainty are propagated hierarchically. This approach sets new SOTA records for MPJPE/MPJRE on the AMASS sparse VR benchmark while providing well-calibrated per-joint confidence.

FLOW: Optimal Transport-Driven Feature Warping for Generalized Remote Physiological Measurement

FLOW treats "distributional shift" in end-to-end rPPG models during cross-domain deployment as a feature-level Optimal Transport (OT) problem. It first utilizes a lightweight Temporal Refinement Module (TRM) to unify and denoise temporal features across domains, then applies Prototype-based Cross-temporal Optimal Transport (PCOT) using a learnable prototype bank for soft alignment. Coupled with two regularization terms, it achieves cross-domain SOTA on four rPPG benchmarks in a plug-and-play, backbone-agnostic manner.

Focal–General Diffusion Model with Semantic Consistent Guidance for Sign Language Production

Addressing the common flaw in the Gloss-to-Pose stage of Sign Language Production (SLP)—which typically models only global sequences while ignoring fine-grained joint-level dependencies—this paper proposes the Focal–General Diffusion Model (FGDM). By employing a two-stage denoising structure that "focuses on joints first, then coordinates the global sequence," combined with a frame-wise Adaptive Semantic Graph Convolutional Network (ASGCN) and a Semantic Consistent Guidance (SCG) mechanism that injects CTC-based semantic supervision into diffusion training, the model achieves new SOTA performance on PHOENIX14T and USTC-CSL.

Forecasting 3D Scanpaths in Egocentric Video

This paper extends the task of "predicting where a person will look next" from 2D images to egocentric videos for the first time. It defines a new task of predicting future gaze sequences (3D scanpaths) within a 3D world coordinate system and proposes a Transformer architecture that uses the "last observed camera pose" as a canonical frame to fuse video, head pose, and historical gaze, establishing the first baseline on the Aria Digital Twin dataset.

From Intuition to Investigation: A Tool-Augmented Reasoning MLLM Framework for Generalizable Face Anti-Spoofing

The TAR-FAS framework is proposed, reconstructing the Face Anti-Spoofing (FAS) task into a Chain-of-Thought with Visual Tools (CoT-VT) paradigm for the first time. This allows MLLMs to adaptively invoke external visual tools (LBP/FFT/HOG, etc.) during reasoning, upgrading from "intuitive judgment" to "fine-grained investigation," achieving SOTA on the 1-to-11 cross-domain protocol.

HamiPose: Hamiltonian Optimization for Unsupervised Domain Adaptive Pose Estimation

Aiming at training oscillations caused by the "source supervision gradient vs. target consistency gradient" conflict in synthetic \(\to\) real domain pose estimation, HamiPose performs orthogonal decomposition of target gradients by keypoints, uses confidence gating to allow only non-conflicting components, and applies a Hamiltonian optimizer with a symplectic integrator to add "controlled momentum" for suppressing high-frequency jitters, achieving SOTA on multiple UDA pose benchmarks.

Hierarchical Enhancement of Semantic Priors for Disentangled Text-Driven Motion Generation

HESP utilizes an Adaptive Gaussian VAE (AG-VAE) that explicitly decomposes the latent space into multiple semantic sub-manifolds, combined with Dynamic Cross-Modal Memory (DCMM) and Hierarchical Cross-modal Attention (HCA). This makes text-driven 3D human motion generation more controllable and interpretable, outperforming baselines such as SALAD, MoMask, and MDM in FID and R-Precision on HumanML3D and KIT-ML.

How to Take a Memorable Picture? Empowering Users with Actionable Feedback

This paper defines a new task of Memorability Feedback (MemFeed) and proposes MemCoach—a training-free activation steering method for Multimodal Large Language Models (MLLMs). By injecting memorability-aware knowledge into the model's activation space using a teacher-student strategy, it enables MLLMs to generate natural language actionable suggestions for improving photo memorability.

HSI-GPT2: A Dual-Granularity Large Motion Reasoning Model with Diffusion Refinement for Human-Scene Interaction

HSI-GPT2 is a Large Motion Model (LMM) for "unified understanding + generation" of Human-Scene Interaction (HSI). It employs a dual-granularity motion tokenizer to decouple actions into semantic and detail codebooks. By utilizing an LLM as a semantic planner and a diffusion decoder as a de-tokenizer, the model achieves high physical fidelity. Integrated with a Motion Chain-of-Thought (MoCoT) data engine and Group Relative Policy Optimization (GRPO), the model performs step-by-step reasoning, significantly outperforming HSI-GPT on HumanML3D and HUMANISE benchmarks for generation, description, and completion tasks.

HUMAPS-4D: A Multimodal Dataset for HUman Motion Analysis with Physiological and Semantic informations

HUMAPS-4D is a large-scale human motion dataset that synchronizes optical motion capture, multi-view RGB, IMU, instrumented pressure insoles, surface electromyography (sEMG), anthropometry, and three-layer semantic annotations under a unified protocol (32 participants × 30 actions × 10 repetitions × 14 hours = 5.76 million frames). Its goal is to establish a rigorous benchmark for inferring full-body 3D poses/actions from physiological signals like plantar pressure without relying on cameras.

HyperGait: Unleashing the Power of Parsing for Gait Recognition in the Wild via Hypergraph

HyperGait utilizes hypergraph convolution to extract "high-order nonlinear correlations" among body parts and across temporal frame segments within gait parsing sequences (GPS). Using only the single parsing modality as input, it achieves a 80.5% Rank-1 accuracy on the real-world Gait3D dataset, surpassing the previous parsing-based SOTA (MultiGaitP) by 4.1 percentage points.

IMU-HOI: A Symbiotic Framework for Coherent Human-Object Interaction and Motion Capture via Contact-Conscious Inertial Fusion

IMU-HOI treats "hand-object contact" as a first-class probabilistic signal. Starting from sparse IMUs attached to the body (6 units) and the object (1 unit), a three-stage fusion pipeline simultaneously recovers full-body human poses and 6-DoF object trajectories, reducing object trajectory error by 44%–64% compared to strong baselines across three HOI benchmarks.

Learning Effective Sign Features without Text for Gloss-free Sign Language Translation

This paper proposes SignDINO—a "sign-aware" pre-training strategy adapted from DINO self-distillation. By providing the teacher with only global frames and the student with local masked views of hands/faces, the model is forced to infer discriminative local cues from global frames alone. This enables pre-training of a sign language tokenizer entirely without gloss or text annotations, achieving or exceeding SOTA performance on four public GFSLT datasets that usually rely on text-based pre-training.

Learning to Diversify and Focus: A Reinforcement Framework for Open-Vocabulary HOI Detection

To address the "query overfitting to seen classes" and "diffuse CLIP attention" issues in open-vocabulary human-object interaction (OV-HOI) detection, this paper proposes the SD-IF framework. It utilizes reinforcement learning (RL)-driven semantic perturbations to push queries out of seen semantic clusters and employs an actor-critic mechanism to "focus" attention on actual interaction regions. Experiments show that ours significantly outperforms previous SOTAs in unseen class mAP on HICO-DET and SWIG-HOI.

MAMMA: Markerless Accurate Multi-person Motion Acquisition

MAMMA is a markerless multi-person motion capture pipeline: starting from multi-view videos, it uses a Transformer (MammaNet) with independent queries for each landmark to predict 512 contact-aware and visibility-aware dense 2D surface landmarks. By fitting SMPL-X to these landmarks, it achieves an accuracy within 0.862mm of commercial marker-based systems (Vicon) in close-range two-person interaction scenarios, while eliminating the need for tedious marking and manual data cleaning.

MFEN: Multi-Frequency Expert Network for Visible-Infrared Person Re-ID

To address the challenge in visible-infrared person re-identification where "illumination differences span across multiple frequency bands and the optimal band varies by sample," MFEN utilizes a Mixture-of-Experts (MoE) structure with multiple frequency band experts and a gating mechanism to adaptively fuse frequency domain cues per sample. Complemented by image-level Random Frequency Augmentation (RFA) and optimization-level Frequency-assisted Optimization (FAO), it achieves or approaches SOTA performance on three VI-ReID datasets.

MGDHand: Multi-Granularity Prior-to-Inertial Distillation Framework for Sequential 3D Hand Pose Estimation from Sparse IMUs

To address the highly ill-posed problem of directly regressing dense hand poses from sparse IMUs due to the semantic gap, MGDHand pre-trains a MANO-IMU fusion teacher to encode priors into three categories: static shape, dynamic pose, and temporal motion. It then employs multi-granularity decoupled distillation (SSD/DPD/TMD) to transfer these priors to an IMU-only student in their respective semantic domains. On the VIHand dataset, this reduces MPJPE by 40.7% compared to a student without distillation.

MimicTalker: A Multimodal Interactive and Memory-Enhanced Framework for Real-Time Dyadic 3D Head Generation

MimicTalker focuses on 3D head motion generation for "real-time dyadic conversations." It employs frame-by-frame causal processing combined with Gated Multi-scale Memory (MICE) to achieve zero-latency perception of the interloutor. Intent and topic semantics extracted by an LLM are used for Semantics-augmented Dynamic Interaction (SDI) to modulate speaker features. Furthermore, a Semantics-guided Motion Style Memory (MSM), utilizing an "intent-as-key, style-as-value" external memory bank, maintains motion style consistency during long conversations. This allows for the generation of natural, coherent, and style-consistent real-time reactions across both 25-second short clips and 6-minute long dialogues, achieving 10%–30% improvements over methods like DualTalk on most metrics.

MoBind: Motion Binding for Fine-Grained IMU-Video Pose Alignment

MoBind utilizes hierarchical contrastive learning to align wearable IMU signals with 2D skeleton motion extracted from video. By aligning IMUs with "skeleton motion" instead of raw pixels to filter out irrelevant backgrounds, decomposing the body into parts for specific IMU pairing, and employing a three-level (token/local/global) contrastive strategy with a masked token prediction task, MoBind significantly outperforms strong baselines in cross-modal retrieval, sub-second temporal synchronization, person/part localization, and action recognition.

MotionMaster: Generalizable Text-Driven Motion Generation and Editing

MotionMaster treats human motion as a new modality integrated into the shared vocabulary of a pre-trained multimodal large language model (Qwen2.5-VL). It utilizes a 10,000-hour annotated motion dataset (MotionGB) and an FSQ discretizer that balances local joint precision with global trajectory consistency. By employing an end-to-end autoregressive model to perform both text-driven motion generation and editing simultaneously, it achieves 41.6% higher semantic consistency for multi-motion sequences and 20.8% higher body part composition compared to prior methods.

MS^2Gait: A Multi-Scale Spatio-Temporal Fusion Network for LiDAR-based Gait Recognition

MS²Gait directly performs gait recognition on raw LiDAR point clouds. Leveraging "four complementary interaction strategies," it enables spatially distant yet semantically related body parts (e.g., contralateral arm and leg) to communicate. It then employs a "similarity-driven temporal-enhanced Transformer" to adaptively weight frames based on motion consistency, achieving 93.5% and 83.1% Rank-1 accuracy on SUSTech1K and FreeGait, respectively, setting a new SOTA for raw point cloud-based gait recognition.

Multi-level Causal LLM-based Text-to-Motion Generation with Human Alignment (MoTiGA)

MoTiGA addresses three primary shortcomings of LLM-based text-to-motion generation—fine-grained quantization errors, the representation mismatch between "causal LLMs" and "non-causal VQ-VAE," and the lack of human preference alignment. These are resolved through Causal Residual Quantization (Causal RVQ-VAE), time-lagged causal prediction, and Multi-level Hybrid weighted Preference Optimization (MHPO). This approach reduces the FID by 82.3% on HumanML3D and 64.7% on KIT-ML compared to other LLM-based methods.

Occluded Human Body Capture with Frequency Domain Denoising Prior

3D human motion capture from monocular occluded video is reformulated as a "wavelet coefficient selection" problem. Uncertainty of occluded keypoints is characterized using Gaussian distributions, and a frequency-domain diffusion prior is utilized to select credible coefficients in the Discrete Wavelet Transform (DWT) domain, enabling consistent and periodicity-preserving motion recovery under long-term occlusion.

Omni-Supervised Motion Editing: Balancing Change and Invariance through Positive-Negative Learning

OmniME addresses text-driven human motion editing by decomposing supervision into two complementary branches: "positive supervision" (retrospective intermediate feature supervision + similarity-based motion preservation) and "negative supervision" (triplet semantic alignment). Within a diffusion framework, it simultaneously constrains "what to change" and "what to keep," reducing the Average Rank (AvgR) from 20.88 to 13.06 on MotionFix and from 29.05 to 22.77 on STANCE Adjustment.

Open the Motion Door: Atomic Motion Decomposition and Recomposition for Open-Vocabulary Motion Generation

To address the poor generalization of Text-to-Motion (T2M) models on out-of-distribution text, this paper proposes an "Atomic Motion Decomposition-Recomposition" framework. It decomposes arbitrary raw text into low-level "atomic motion" descriptions across different body parts and time intervals, then learns to recompose these atomic motions into complete sequences. Using only HumanML3D for training, it significantly outperforms SOTA models on two out-of-distribution datasets (IDEA400, Mixamo).

OSMO: Open-vocabulary Self-eMOtion Tracking

This paper proposes a new task, "First-person Self-emotion Tracking"—inferring the wearer's evolving emotions over time from the multimodal streams of smart glasses (speech, visual environment, dialogue text, and eye movements). It introduces the OSMO dataset (110 hours, the first and largest first-person emotion dataset with per-subject timelines), the OSMO benchmark (5 tasks), and the OSIRIS model (the first LMM to join video/audio/dialogue/eye IR and use emotional history for temporal reasoning), significantly setting new SOTA results across all metrics.

PAMotion: Physics-Aware Motion Generation for Full-Body Interaction with Multiple Objects

PAMotion utilizes the physical intuition that "object acceleration exposes the contact state" to design a soft physics-aware interaction loss. Combined with a coarse-to-fine two-stage conditional diffusion, it ensures text-driven full-body multi-object interaction motions are semantically aligned while eliminating issues like hand interpenetration and floating objects, achieving SOTA on HIMO and ParaHome.

Pose-guided Enriched Feature Learning for Federated-by-camera Person Re-identification

Addressing the "one client = one camera, limited pose visibility" scenario in federated-by-camera person Re-identification, this paper proposes a Pose Extraction Module (PEM) to decouple features into "pose-related" and "pose-unrelated" components. By swapping pose components across identities to synthesize "pose-changed" hard positive samples, and employing Pose-guided Knowledge Distillation (PKD), Semantic Consistency Maintenance (SCM), and Compatibility Regularization (CR) for decoupling quality and global compatibility, the method compensates for the lack of pose diversity in contrastive learning, achieving SOTA results on Market1501 and MSMT17 for FedReID.

PRISM: Learning a Shared Primitive Space for Transferable Skeleton Action Representation

PRISM represents skeleton actions as a "weighted combination of reusable atomic motion primitives" (primitive coefficient space). It first learns this physically interpretable, view-invariant structured representation using multi-view synthetic data through a generative objective, then sequentially transfers the same representation to classification and frame-wise detection via lightweight task heads. It consistently outperforms specialized models on long-tail, multi-label, and multi-view real-world datasets.

Progressive Guessing to Fixed Point: Rethinking Human Motion Prediction with Deep Equilibrium Models

MotionDEQ reformulates the cascaded framework of "multi-stage progressive guessing" in human motion prediction into a fixed-point solving problem within an implicit layer. This is equivalent to infinite refinement stages but requires only \(O(1)\) training memory. By injecting Euclidean equivariance into this equilibrium process and utilizing the temporal coherence of adjacent predictions to reuse the previous fixed point as a "warm-start," it achieves SOTA accuracy ([email protected] on Human3.6M) with fewer than 300K parameters, saving more than 2x training memory compared to multi-stage competitors.

Push-and-Step: From RL-Based Balance Recovery to Physical Simulation of Dense Crowds

A two-stage deep reinforcement learning (RL) framework is used to train full-body physical humanoid agents. In the first stage, agents learn "stepping to recover balance" after being pushed through motion imitation and physical balance rewards. In the second stage, the policy is fine-tuned using AdaptNet with a "hand-to-shoulder contact" heuristic. This allows agents in dense crowds to socially dissipate impact by pushing or leaning on neighbors, successfully reproducing phenomena such as force propagation, falls, and crowd crushes in a purely physical simulation.

Real-Time Multimodal Fingertip Contact Detection via Depth and Motion Fusion for Vision-Based Human-Computer Interaction

This paper does not invent a new network but utilizes a specifically collected dataset of 53,300 millimeter-level RGB-depth pairs to fine-tune existing monocular depth models for near-field fingertip scenarios. By layering a "depth + motion" fusion velocity-gated state machine, the system reduces depth error from 12.3 mm to 3.84 mm (a 68% reduction) using only a standard RGB camera. It achieves a 94.4% F1-score for contact detection, enabling "blind typing" on a tabletop at 45.6 WPM with a 3.1% character error rate, approaching the performance of specialized depth hardware and commercial VR input.

RegFormer: Transferable Relational Grounding for Efficient Weakly-Supervised HOI Detection

RegFormer transforms weakly-supervised HOI detection from "enumerating all human-object pairs and cropping regions for classification" to "grounding human-object relations as queries on CLIP spatial feature maps and gating non-interacting pairs with interactiveness scores." Trained only with image-level annotations, it transfers directly to instance-level detection with a single backbone forward pass, achieving 38.14 mAP on HICO-DET with H-DETR, surpassing fully-supervised methods.

Region-Aware Instance Consistency Learning for Micro-Expression Recognition

This paper views a micro-expression sequence as a multi-instance set composed of "onset frame + multiple middle frames." By using a Siamese network to force the alignment of attention maps across different instances (IRC) and employing learnable facial queries to uncover neglected weak activation regions (MRD), the method completely eliminates the need for expensive apex frame annotations and outperforms state-of-the-art (SOTA) methods across four public datasets.

Render-to-Adapt: Unsupervised Personal Adaptation for Gaze Estimation

This paper argues that the group-level assumptions of mainstream Unsupervised Domain Adaptation (UDA) are disconnected from real-world scenarios where systems serve only one new user at a time. It proposes a new paradigm, Unsupervised Personal Adaptation (UPA), and constructs a Render-Cycle consistency self-supervision signal using a fixed-parameter differentiable renderer. By rendering predicted gaze into a new image and reading back the iris position, the method uses the consistency of iris locations to backpropagate and correct gaze deviations. This approach achieves stable gains for every user in cross-dataset person-specific adaptation, significantly outperforming existing SOTAs.

SignPR: A Progressive Vector-Quantized Diffusion Framework for Sign Language Production

Addressing the gloss-free Text2Pose task, SignPR proposes a "structural + temporal" dual-progressive vector-quantized diffusion framework. It utilizes a structured VQVAE to decompose each frame's pose into semantic-level (global) and regional-level (hand/face/body) discrete tokens. The diffusion process first generates semantically consistent coarse poses before refining regional details. During inference, block-level causal progressive refinement is employed to ensure temporal coherence. SignPR outperforms previous T2P methods on Phoenix14T, CSL-Daily, and USTC-CSL datasets.

Spatial-Frequency Collaborative Learning for Occluded Visible-Infrared Person Re-Identification

Aiming at Occluded Visible-Infrared Person Re-Identification (Occluded VI-ReID), this paper proposes the SFCL framework: using FFT to decompose features into amplitude (encoding modality appearance) and phase (preserving identity structure). It aligns modalities in the frequency domain using Optimal Transport, injects frequency structural cues back into spatial features, and employs a frequency-contrastive and semantic-consistent FAD loss. The method outperforms previous SOTA on two self-constructed occlusion datasets (Occ-SYSU-MM01 All-Search Rank-1 65.97%, +4.31%).

SSM-Aware Token-Efficient VMamba via Adaptive Patch Pruning and Merging for Person Re-Identification

TE-VMamba leverages the SS2D state update intensity (step size \(\Delta\)) and token similarity to guide token reduction. It prunes redundant tokens that contribute minimally to the state in shallow layers based on \(\Delta\) and merges semantically similar tokens in deep layers. On Market-1501, it reduces FLOPs by over 60% while Rank-1 accuracy actually increases.

SyncDreamer: Controllable and Expressive Avatar Generation Beyond the Talking Head

SyncDreamer utilizes a Diffusion Transformer framework to generate identity-preserving, emotionally expressive avatar videos with fine-grained text control over gestures and gaze, using only a single reference image, audio, and text prompts. It locks identity through a visual adapter (with attention localization loss), converts speech rhythm/energy into expression drivers via an audio dynamics encoder, and transforms short text into actionable movement instructions through a GRPO-trained cross-modal prompt enhancer, achieving SOTA on both portrait and full-body benchmarks.

SyncMos: Scalable Motion Synchronisation for Multi-Agent Scene Interaction

SyncMos utilizes an LLM event planner to decompose natural language instructions into temporal dependency graphs. By applying time-warping and Diffusion Posterior Sampling (DPS) as post-processing without retraining the single-agent diffusion motion model, it aligns the actions of an arbitrary number of agents (e.g., handing over objects) in time, achieving scalable multi-agent 3D scene interaction generation.

Through the Frequency Lens: Cross-Domain Generalisable Gaze Estimation with Adaptive Modulation

This paper dissects cross-domain degradation in gaze estimation from a frequency-domain perspective. It discovers that facial images contain both generalisation-assisting "gaze-related frequency bands" and generalisation-harming "interference bands," and their distribution shifts across datasets. Based on this, the FGAL framework is proposed: a lightweight attention network generates a learnable frequency modulation map for each image to adaptively suppress interference bands (AISM), and intra-source histogram matching expands the spectrum distributions seen during training (SDM). Across four cross-domain settings, FGAL reduces error by up to 28.2% compared to the baseline and up to 19.5% compared to the state-of-the-art (SOTA).

Towards Cross-Modal Preservation, Consistency and Alignment for Privacy-Preserving Visible-Infrared Person Re-Identification

This paper introduces a new task, PP-VI-ReID (Privacy-Preserving Visible-Infrared Person Re-Identification), and proposes a PPA framework to address two major challenges: "anonymization destroying identity information" and "inconsistent anonymization distortion across modalities." The KPR module utilizes human pose priors for structure-aware precise anonymization, while the DCMA module treats anonymization perturbations as learnable stable offsets to align cross-modal features. The method significantly outperforms a modified version of SecureReID on SYSU-MM01 and RegDB, establishing a strong baseline.

Towards Decompositional Human Motion Generation with Energy-Based Diffusion Models

DeMoGen reverses "text-to-motion" generation: it utilizes energy-based diffusion models to decompose a holistic motion into several semantically interpretable motion concepts (e.g., "walking in a Z-shape" + "waving left hand") without decomposition-level ground truth. These concepts can be freely recombined to generate unseen motions. It achieves improvements across text-to-motion, composition, and multi-concept tasks on HumanML3D and MTT.

Towards Highly-Constrained Human Motion Generation with Retrieval-Guided Diffusion Noise Optimization

Addressing extremely difficult spatial-temporal/numerical constraints (e.g., "passing through a 0.4m narrow gap," "walking 4 meters in exactly 6 steps"), this paper introduces a retrieval channel into the training-free Diffusion Noise Optimization (DNO) framework. It first parses the most difficult constraints via relational task analysis, retrieves reference motions from a dataset to invert them into reference noise, and finally blends random and retrieved noise using a reward-guided mask as a superior initialization. This significantly reduces constraint errors compared to vanilla DNO.

Towards Storytelling Animations: Joint Synthesis of Human and Camera Motions

This paper proposes the first framework to jointly generate "3D motions of two interacting characters" and "camera motion" in a unified diffusion model. Applying a three-branch parallel backbone and three pairwise interaction modules, the framework explicitly models character-character and character-camera mutual influences. A combined dataset of 7,228 character-camera sequences, comprising real movie clips and synthetic data, is constructed. The proposed approach outperforms specialized methods across all three dimensions: character motion, camera motion, and their coordination.

Unifying Precise Keyframes and Semantic Control via Multi-level Diffusion

Addressing the pain point that "text only provides high-level semantics, while keyframes provide precise spatial-temporal constraints but are difficult to coordinate with text," this paper proposes a multi-level diffusion framework: local guidance uses individual keyframes to refine local transitions around them, while global guidance merges text with the implicit temporal cues of the entire keyframe sequence into a unified representation to control global dynamics; during inference, a velocity-ratio-preserving trajectory refinement combined with diffusion imputation-based pose refinement is proposed to convert keyframe constraints from "soft approximations" to "strictly satisfied zero-error constraints," which also naturally supports training-free, semantic-preserving motion editing, reducing Keyframe Error to 0 cm on HumanML3D.

Unlocking Motion from Large Vision Models with a Semantic and Kinematic Duality for Gait Recognition

GaitMax utilizes a frozen DINOv3 Large Vision Model (LVM) to concurrently deploy a "Semantic Branch" (capturing global, order-invariant silhouettes) and a "Kinematic Branch" (tracking spatio-temporal trajectories of body parts via learnable queries). It incorporates a Conditional Decoupling Loss (CDLoss) to suppress shortcuts by de-correlating gait embeddings from textual descriptions of distractors (e.g., clothing, viewpoint) using second-order statistics. Supported by the self-constructed GCaption dataset with natural language labels, GaitMax achieves new SOTA performance across multiple cross-domain gait benchmarks.

VRCLIP: Multimodal Canonical Correlation Alignment for CLIP-Driven Vision-Radio Person Re-Identification

VRCLIP fuses RGB images and low-frequency Radio Frequency (RF) signals for person re-identification. The core idea is to replace "distribution alignment" with "correlation maximization" using Canonical Correlation Analysis (CCA), aligning shared semantics while retaining modality-specific features. Combined with illumination-decoupled fine-tuning of the CLIP vision encoder and RF-anchored adaptive gated fusion, it achieves 93.9% mAP on a self-collected dataset of 650,000 VRR pairs.

4DEquine: Disentangling Motion and Appearance for 4D Equine Reconstruction from Monocular Video

The 4DEquine framework is proposed to disentangle 4D reconstruction of equines from monocular video into two sub-problems: dynamic motion estimation (AniMoFormer) and static appearance reconstruction (EquineGS). It achieves SOTA performance on real-world data while being trained only on synthetic data.

Anatomical Domain Shifts: Test-time Heterogeneous Adaptation for 3D Human Pose Prediction

Addressing Continuous Test-time Adaptation (CTTA) for 3D Human Pose Prediction (HPP), this paper identifies the overlooked fact that "domain shifts are concentrated in specific body parts rather than occurring uniformly across the whole body." It proposes TT-HA: decomposing model parameters into five anatomical subsets (left/right arms, left/right legs, torso), using Instance Normalization (IN) statistics combined with Earth Mover's Distance (EMD) to measure online domain changes for each part. Based on these measurements, self-supervised fine-tuning is applied to parts with minor shifts, while parameters for parts experiencing abrupt changes are rolled back to the source model. This achieved a 4.7% reduction in overall MPJPE and a 9.2% reduction in limb errors.

AnyLift: Scaling Motion Reconstruction from Internet Videos via 2D Diffusion

AnyLift introduces a two-stage framework—synthesizing multi-view 2D motion data followed by training a camera-conditioned multi-view 2D diffusion model—to lift 2D keypoints from monocular dynamic-camera internet videos into 3D human motion and Human-Object Interaction (HOI) in world coordinates. Without any 3D supervision, it reconstructs rare actions (e.g., gymnastics, martial arts) seldom found in MoCap datasets.

ARMFlow: AutoRegressive MeanFlow for Online 3D Human Reaction Generation

This work introduces the "single-step generation" MeanFlow paradigm to the human motion domain for the first time. By utilizing an autoregressive structure consisting of a "causal context encoder + lightweight MLP velocity predictor" combined with Bootstrapped Causal Encoding (BSCE) to suppress error accumulation, online 3D human reaction generation is achieved within a single inference step. The method reduces FID by approximately 30% compared to existing online methods while maintaining the fastest speed.

AvatarPointillist: AutoRegressive 4D Gaussian Avatarization

AvatarPointillist proposes an autoregressive (AR) generation framework to construct 4D Gaussian avatars. It utilizes a decoder-only Transformer to generate 3DGS point clouds point-by-point (including binding information) and employs a Gaussian Decoder to predict rendering attributes. This approach breaks the limitations of fixed template topologies and enables adaptive point density adjustments, outperforming baselines like LAM and GAGAvatar on the NeRSemble dataset.

Bringing Your Portrait to 3D Presence

Utilizing a Dual-UV representation that projects image features into a canonical UV space, paired with a factorized "3D rendering + 2D generation" synthetic data manifold and a robust proxy mesh tracker, this work enables the reconstruction of animatable 3D Gaussian avatars from a single portrait (head, half-body, or full-body). The model generalizes to real-world photos despite being trained exclusively on synthetic data.

CARI4D: Category Agnostic 4D Reconstruction of Human-Object Interaction

Proposes CARI4D, the first category-agnostic method to reconstruct metric-scale 4D human-object interactions from monocular RGB video—comprising object shape reconstruction, pose tracking, hand contact reasoning, and physically constrained optimization, generalizing zero-shot to unseen categories.

CGHair: Compact Gaussian Hair Reconstruction with Card Clustering

CGHair is proposed to achieve over 200x appearance parameter compression and 4x hair reconstruction acceleration while maintaining comparable visual quality through hair-card-guided hierarchical clustering and a shared Gaussian appearance codebook.

Clay-to-Stone: Phase-wise 3D Gaussian Splatting for Monocular Articulated Hand-Object Manipulation Modeling

To address the unstable optimization caused by the strong coupling of "geometry" and "articulated motion" in monocular video, this paper proposes the Clay-to-Stone dual-phase 3DGS framework. It first utilizes a "soft clay" phase (CLAY) for fine-grained, semantic-aware free deformation to explore structure and motion, followed by a "stone" phase (STONE) that imposes rigid constraints and explicitly estimates axes, pivots, and joint angles. This approach achieves SOTA geometric reconstruction and realistic rendering on the ARCTIC articulated object dataset.

CrossHOI: Learning Cross-View Representations for Monocular 3D Human-Object Interaction Reconstruction

Starting from a single image, CrossHOI "imagines" the image features from another perspective and uses these generated cross-view features to complement the geometric information of human-object mutual occlusion regions. This simultaneously improves mesh reconstruction accuracy and contact region estimation in monocular 3D human-object interaction (HOI) reconstruction, achieving state-of-the-art (SOTA) results on BEHAVE and InterCap, with particularly significant improvements in occluded scenarios.

CrowdGaussian: Reconstructing High-Fidelity 3D Gaussians for Human Crowd from a Single Image

CrowdGaussian proposes a unified framework for reconstructing multi-person 3D Gaussian Splatting (3DGS) representations from a single image. It recovers complete geometry in occluded regions through a self-supervised adapted Large Occlusion-aware Reconstruction Model (LORM) and enhances texture detail quality using a single-step diffusion refiner (CrowdRefiner) trained with Self-Calibrated Learning (SCL).

CUBE: Representing 3D Faces with Learnable B-Spline Volumes

Ours proposes CUBE (Control-based Unified B-spline Encoding), a hybrid geometric representation combining B-spline volumes with learnable high-dimensional control features. It achieves editable, high-precision 3D face reconstruction and scan registration through two-stage decoding (B-spline basis interpolation + lightweight MLP residuals).

DuoMo: Dual Motion Diffusion for World-Space Human Reconstruction

DuoMo is proposed to decompose world-space human motion reconstruction into two independent diffusion models: a camera-space model extracts generalized camera-coordinate motion from video, and a world-space model refines lifted noisy proposals into globally consistent world-coordinate motion. By directly generating mesh vertex motion instead of SMPL parameters, it reduces W-MPJPE by 16% on EMDB and 30% on RICH.

Egocentric Visibility-Aware Human Pose Estimation

Addressing the "frequently invisible keypoints" issue in egocentric human pose estimation for head-mounted devices (HMDs), this paper constructs Eva-3M, the first large-scale real-world dataset with visibility annotations (3 million frames, 435,000 visibility labels). It proposes EvaPose, which explicitly predicts the visibility of each keypoint and weights the loss accordingly, reducing the MPJPE of visible keypoints from 49.8mm in FRAME to 34.2mm.

ELITE: Efficient Gaussian Head Avatar from a Monocular Video via Learned Initialization and Test-time Generative Adaptation

ELITE synthesizes drivable, photorealistic 2D Gaussian head avatars from casual monocular videos. The core contribution is coupling "feed-forward 3D data prior initialization" with "rendering-guided single-step diffusion enhancement" to make the two types of priors complementary—the former provides fast, identity-preserving initialization, while the latter completes unobserved views and expressions. Ultimately, it outperforms existing methods in image quality and identity preservation while being 60 times faster than 2D generative prior-based methods.

EmoDiffTalk: Emotion-aware Diffusion for Editable 3D Gaussian Talking Head

EmoDiffTalk maps the "emotion-to-expression" transformation onto the explainable Action Unit (AU) encoding space. It utilizes AU-prompted Gaussian diffusion to drive speech into fine-grained dynamic 3D Gaussian talking heads and implements "one-sentence emotion editing" via a text-to-AU controller. It surpasses Prev. SOTA in rendering fidelity, lip synchronization, and emotional controllability on EmoTalk3D and RenderMe-360.

EmoTaG: Emotion-Aware Talking Head Synthesis on Gaussian Splatting with Few-Shot Personalization

EmoTaG is proposed, an emotion-aware 3D talking head synthesis framework based on a FLAME-Gaussian structural prior and a Gated Residual Motion Network (GRMN). It achieves few-shot personalization with only 5 seconds of video while balancing emotional expression, lip-sync, and geometric stability.

Enhancing Hands in 3D Whole-Body Pose Estimation with Conditional Hands Modulator

Ours proposes the Hand4Whole++ modular framework, which injects features from a pre-trained hand estimator into a frozen whole-body estimator via a lightweight CHAM module. This achieves precise wrist orientation prediction and transfers fine finger joints and hand shapes from a hand model using differentiable rigid alignment.

Feed-forward Gaussian Registration for Head Avatar Creation and Editing

MATCH utilizes a transformer to directly predict "Gaussian Splatting textures under dense semantic correspondence" from calibrated multi-view images in 0.5 seconds. It bypasses the time-consuming mesh tracking and per-subject optimization—which typically take hours or even a day in traditional workflows—and directly enables cross-identity/cross-expression applications such as avatar creation, interpolation, semantic editing, and expression transfer.

Feed-Forward One-Shot Animatable Textured Mesh Avatar Reconstruction

MeshLAM utilizes a feed-forward Transformer to reconstruct a 3D head mesh avatar with high-fidelity textures and direct animatability from a single image in one pass. By employing a "dual shape/texture branch + GRU iterative decoding + input-to-UV back-projection guidance," it avoids test-time optimization and mesh collapse while surpassing Gaussian-based LAM in both quality and speed.

FHAvatar: Fast and High-Fidelity Reconstruction of Face-and-Hair Composable 3D Head Avatar from Few Casual Captures

FHAvatar utilizes a feed-forward aggregation Transformer to reconstruct a face-and-hair composable/decomposable 3D Gaussian head avatar from a few casual mobile captures within minutes. Planar Gaussians in the UV space are used for the face, while scalp-anchored strand-based Gaussians are used for the hair. The two components are explicitly decoupled in the texture space, supporting real-time driving, hairstyle transfer, and texture stylized editing.

FISHuman: Fine-grained Single-image 3D Human Reconstruction via Multi-view 4D Remeshing

FISHuman utilizes a "3D-aware dual-stream video diffusion model" to expand a single photo into multi-view aligned RGB+normal sequences. It then employs a "4D Remeshing" module to transform pixel drifts from inconsistent multi-view frames into controllable per-vertex deformations. This allows for the reconstruction of 3D humans with fine geometry, realistic textures, and animation-ready meshes from a single image, outperforming SOTAs like PSHuman and Human3Diffusion in geometry and appearance metrics on 2K2K / Sizer.

FlexAvatar: Flexible Large Reconstruction Model for Animatable Gaussian Head Avatars with Detailed Deformation

FlexAvatar utilizes a transformer-based Large Reconstruction Model (LRM) combined with Structured Head Query tokens to aggregate an arbitrary number of single or sparse input images—without camera poses or expression labels—into a unified UV-space Gaussian avatar. A lightweight UNet driven by UV position maps decodes expression-related deformations in real-time. Together with data distribution adjustment and a 10-second test-time refinement, it achieves SOTA 3D consistency and dynamic detail realism.

Fresco: Frequency-Spatial Consistent Optimization for Fine-Grained Head Avatar Modeling

Fresco does not modify the underlying representation of head avatars but focuses on training dynamics: it employs a Laplacian pyramid for a "low-to-high" frequency curriculum and incorporates differentiable UV-baking to align multi-view renderings to a shared texture atlas. This suppresses early pseudo high-frequency artifacts and eliminates cross-view drifting, achieving SOTA results in both novel-view and self-reenactment metrics (PSNR/LPIPS) on the NeRSemble dataset.

GauMVC: Generative Decoupled Gaussian Representation for Human-centric Multi-view Video Compression

GauMVC explicitly decouples "human-centric multi-view video" into two parts: a static background and a dynamic human body. The background is represented by a one-time 3D Gaussian field, while the human body is driven as a personalized Gaussian avatar using only a few key views and frame-wise SMPL pose parameters. This shifts compression from "removing pixel redundancy" to "transmitting semantic parameters and generating frames," enabling high-fidelity free-viewpoint video synthesis even at extremely low bitrates.

GeoDiff4D: Geometry-Aware Diffusion for 4D Head Avatar Reconstruction

Starting from a single portrait, GeoDiff4D enables a diffusion model to jointly generate portrait frames and corresponding surface normals. These "Images + Normals + Expression Latents" are then fed into a 3D Gaussian reconstruction to distill the implicit 3D geometric priors from the diffusion model into an animatable 4D avatar. This approach significantly outperforms existing methods in identity preservation, expression recovery, and cross-view consistency.

Glove2Hand: Synthesizing Natural Hand-Object Interaction from Multi-Modal Sensing Gloves

The Glove2Hand framework translates egocentric videos of users wearing sensing gloves into realistic bare-hand videos while preserving tactile and IMU signals. By constructing HandSense, the first multi-modal hand-object interaction dataset, it significantly improves downstream performance for bare-hand contact estimation and occluded hand tracking.

HandDreamer: Zero-Shot Text to 3D Hand Model Generation using Corrective Hand Shape Guidance

HandDreamer is the first zero-shot "Text-to-3D Hand Model" method. It utilizes MANO hand models for low-score initialization, employs 2D hand skeletons as ControlNet conditions to compress the number of modes in the probability distribution, and introduces a corrective hand shape (CHS) loss to rectify geometry throughout the SDS process. This enables the generation of view-consistent, highly detailed, and animatable 3D hands without introducing Janus multi-face artifacts.

High-Fidelity Mobile Avatars with Pruned Local Blendshapes

This work pushes the pose-dependent appearance decoding of 3DGS-based full-body digital humans to the extreme using "local linear blendshapes + 90% blendshape pruning." Through end-to-end training (without pre-trained large models), it achieves 2K resolution at 120 FPS in mobile browsers with a model size of only 19.4 MB.

Human Geometry Distribution for 3D Animation Generation

This paper proposes a two-stage generation framework that first compresses per-frame 3D human geometry into a compact latent using an improved "Human Geometry Distribution (HuGeoDis)" and then generates short-term transitions in the latent space via identity-conditioned autoregressive diffusion. This approach synthesizes 3D human sequences with fine-grained clothing wrinkles, natural dynamics, and consistent identity even with scarce 3D animation data (reducing reconstruction Chamfer distance by approximately 90% and improving user study scores by 2.2x).

Human Interaction-Aware 3D Reconstruction from a Single Image

The HUG3D framework is proposed to achieve high-fidelity textured 3D reconstruction of multiple interacting humans from a single image through perspective-orthogonal view transformation, group-instance multi-view diffusion models, and physics-aware geometric reconstruction, significantly outperforming existing methods on metrics such as CD, P2S, and NC.

HumanNOVA: Photorealistic, Universal and Rapid 3D Human Avatar Modeling from a Single Image

HumanNOVA transfers the Large Reconstruction Model (LRM) paradigm for general objects to the human domain. Utilizing a "dual-modal token conditioning + tri-plane" feed-forward architecture, it reconstructs photorealistic 3D humans from a single image in under 1 second. The study also introduces a scalable data generation pipeline that expands training assets to 100,000 (roughly a 20x increase), achieving a 40%+ relative improvement in LPIPS across three benchmarks.

HyperGaussians: High-Dimensional Gaussian Splatting for High-Fidelity Animatable Face Avatars

HyperGaussians is proposed to extend 3DGS to high-dimensional multivariate Gaussians. It models expression-dependent attribute variations through conditional distributions and utilizes an inverse covariance trick for efficient conditioning. As a plug-and-play module integrated into FlashAvatar and GaussianHeadAvatar, it significantly improves the quality of high-frequency details.

Illumination-Consistent Human-Scene Reconstruction from Monocular Video

This paper jointly reconstructs an animatable human and a static scene from monocular video using 3DGS. The core involves introducing a "light volume" to provide spatially-varying local illumination clues for human PBR and an implicit shadow module to decouple soft shadows cast by the human onto the scene, ensuring human-scene consistency in illumination and shadows while supporting relighting and cross-scene synthesis.

MetricHMSR: Metric Human Mesh and Scene Recovery from Monocular Images

MetricHMSR simultaneously recovers human SMPL meshes and 3D scenes with real physical scales (metric) from a single monocular image. The core involves explicitly encoding camera intrinsics and cropping information into the network using a "boundary camera ray map," decoupling local pose from global position via HumanMoE, and calibrating monocular depth using the recovered metric human as a geometric anchor to achieve SOTA in both human mesh recovery and metric human-scene reconstruction tasks.

MimiCAT: Mimic with Correspondence-Aware Cascade-Transformer for Category-Free 3D Pose Transfer

This paper proposes MimiCAT, a cascaded Transformer framework that learns flexible many-to-many soft correspondences via semantic keypoint labels. Combined with PokeAnimDB, a million-scale multi-category motion dataset, it achieves high-quality 3D pose transfer across categories (e.g., humanoid to quadruped/bird) for the first time.

MoCapAnything: Unified 3D Motion Capture for Arbitrary Skeletons from Monocular Videos

Given a monocular video and an arbitrary 3D skeletal asset (human/animal/robot/toy) as a prompt, MoCapAnything first predicts per-joint 3D trajectories and then solves for the asset's specific skeleton rotation (e.g., BVH) using constraint-aware Inverse Kinematics (IK). This achieves unified motion capture and cross-species retargeting across heterogeneous skeletons, reducing the MPJPE of unseen species from 7.42cm to 1.76cm on Truebones Zoo.

Motion-Aware Animatable Gaussian Avatars Deblurring

The authors propose the first method to reconstruct clear, animatable 3D human Gaussian Avatars directly from blurry videos. This is achieved through a 3D-aware physical blur formation model and an SMPL-based human motion model, which jointly optimize the Avatar representation and motion parameters.

Natural Human Motion Recovery by Aligning High-Order Temporal Dynamics from Monocular Videos

Addressing the issue where monocular human motion recovery results have accurate joint positions but appear either jittery or over-smoothed, this paper proposes HTD-Refine. It uses a lightweight temporal network, PVA-Net, to explicitly predict the 3D velocity and acceleration of each joint from video. These high-order dynamics serve as soft constraints to optimize global trajectories. This plug-and-play approach reduces jitter, suppresses over-smoothing, and improves global accuracy for existing methods like TRAM, GVHMR, and Human3R.

OMG-Avatar: One-shot Multi-LOD Gaussian Head Avatar

OMG-Avatar reconstructs an animatable 3D Gaussian head avatar from a single image in 0.2 seconds. Through "hierarchical coarse-to-fine feature extraction + depth-buffer-guided occlusion-aware fusion + head-shoulder divide-and-conquer modeling," the unified model dynamically switches levels of detail (LOD) at runtime, achieving SOTA reconstruction quality and 85 FPS real-time speed with fewer Gaussian points.

OMGTex: One-stage Multi-style Facial Texture Reconstruction without Geometry Guidance

OMGTex utilizes a DiT-based diffusion model to directly map facial images of any style to editable UV textures. It employs "gradient-guided alignment" during inference to correct UV structural misalignments and achieves partitioned editing through semantic attribution of attention blocks. The process remains independent of 3D geometric priors throughout, demonstrating robustness to occlusions and stylized inputs. It achieves SOTA performance on LPFF/CANVAS with a reconstruction time of 7 seconds per image.

OnlineHMR: Video-based Online World-Grounded Human Mesh Recovery

Proposes OnlineHMR, the first online world-grounded human mesh recovery framework that simultaneously satisfies four criteria: system causality, faithfulness, temporal consistency, and efficiency. It achieves streaming camera-coordinate HMR through sliding window causal learning + KV cache inference, combined with human-centric incremental SLAM and EMA trajectory correction for online global localization.

PAD-Hand: Physics-Aware Diffusion for Hand Motion Recovery

PAD-Hand is proposed as a physics-aware conditional diffusion framework that integrates Euler-Lagrange dynamics residuals into the diffusion process as virtual observations. By estimating joint-wise and frame-wise dynamic variance through last-layer Laplace approximation, it achieves hand motion recovery with both physical plausibility and uncertainty awareness, reducing acceleration error by 50.1% on DexYCB.

PercHead: Perceptual Head Model for Single-Image 3D Head Reconstruction & Editing

PercHead reconstructs 3D-consistent heads from a single image that are robust to extreme viewpoints. The core mechanism is to discard pixel-level supervision such as L1/LPIPS, instead leveraging intermediate features of foundation models (DINOv2 + SAM 2.1) to construct a "pure perceptual loss." Combined with a ViT architecture (a 2D encoder and a 3D decoder initialized with a FLAME template, followed by Gaussian Splatting), it outperforms existing methods in LPIPS, DreamSim, and ArcFace on Ava-256 under extreme viewpoints. Furthermore, it easily extends to disentangled 3D editing ("geometry controlled by segmentation maps + style controlled by text/reference images") simply by replacing the encoder.

PhysHead: Simulation-Ready Gaussian Head Avatars

PhysHead is proposed as the first method to combine physics-driven hair dynamics with animatable 3DGS head avatars. It models the expressible face using FLAME meshes and 3DGS, models hair appearance using strands and 3DGS, and drives hair animation via a physics engine. Layered optimization for the face and hair is achieved through VLM-generated bald images.

PoseMaster: A Unified 3D Native Framework for Stylized Pose Generation

PoseMaster proposes a 3D native approach that unifies pose stylization and 3D generation into an end-to-end framework. It directly utilizes 3D skeletons as pose control signals (instead of 2D skeleton maps), designs a skeleton densification strategy and a Point Transformer encoder to extract fine-grained spatial topological features. Trained through a large-scale "Image-Skeleton-Mesh" triplet data engine, it achieves SOTA results in pose normalization and arbitrary pose stylization.

ProgressiveAvatars: Progressive Animatable 3D Gaussian Avatars

Proposes ProgressiveAvatars, a progressive avatar representation based on adaptive implicit subdivision of template meshes to construct hierarchical 3DGS. It supports progressive transmission and rendering under varying bandwidth and computation constraints—obtaining a usable avatar with only 5% of the data (2.6MB), with subsequent incremental loading smoothly improving quality to levels comparable with SOTA methods.

Recovering Physically Plausible Human-Object Interactions from Monocular Videos

RePHO takes "visually plausible but physically flawed" Human-Object Interaction (HOI) sequences estimated from monocular videos and reenacts them in a physical simulator using reinforcement learning policies. By leveraging "adaptive sampling + bidirectional propagation + online kinematic target updates," it identifies reliable frames from extremely noisy initial values and progressively diffuses physical validity. This results in physically consistent HOI sequences without interpenetration, floating, or jitter, significantly outperforming existing methods on BEHAVE and InterCap datasets.

ReGenHOI: Unifying Reconstruction and Generation for 3D Human-Object Interaction Understanding

ReGenHOI unifies the "reconstruction" (restoring observed contacts from images) and "generation" (synthesizing future interactions from linguistic instructions) of 3D Human-Object Interaction (HOI) into a shared semantic-geometric latent space. By integrating direct 3D point cloud contact reasoning, iterative reasoning trajectories, and a gravitational field diffusion bridge for contact refinement, it simultaneously outperforms SOTA in contact estimation, reconstruction accuracy, and motion generation quality.

Registration-Free Learnable Multi-View Capture of Faces in Dense Semantic Correspondence

MOCHI is the first multi-view dense correspondence face reconstruction framework that does not require pre-registered data for training. By employing a "pseudo-linear inverse kinematics solver + differentiable pointmap/normal loss + dense landmarks trained on synthetic data" trio, it directly learns topology-consistent FLAME meshes from raw scans. Coupled with a lightweight test-time optimization (TTO), its reconstruction accuracy surpasses the very slow and labor-intensive traditional registration pipelines it aims to replace.

Relightable Holoported Characters: Capturing and Relighting Dynamic Human Performance from Sparse Views

RHC utilizes a transformer network, RelightNet, to perform cross-attention between "physics-inspired features (geometry/albedo/shading/view)" and environment lighting to implicitly solve the rendering equation in a single forward pass. It enables photo-realistic, free-viewpoint relighting of dynamic full-body characters with unseen motions from just 4 flat-lit cameras—avoiding slow OLAT-based acquisition and achieving significantly higher clarity than inverse rendering methods.

RelightAnyone: A Generalized Relightable 3D Gaussian Head Model

RelightAnyone proposes a "two-stage" relightable 3D Gaussian head model: it first learns a cross-identity, full-on lit 3DGS avatar from large-scale, easily accessible "flat-lit multi-view facial data", and then trains a mapping network with a small amount of expensive OLAT data to translate the full-on lit Gaussian parameters into relightable RGCA physical reflectance parameters. This eliminates the need to acquire OLAT data for every new subject, enabling reconstruction and arbitrary relighting even from a single in-the-wild photo.

ResiHMR: Residual-Limb Aware Single-Image 3D Human Mesh Recovery for Individuals with Limb Loss

ResiHMR is the first single-image 3D human mesh recovery framework specifically for the amputee population. It utilizes "Residual-Limb Anchor-Factor Optimization" to clip the fixed SMPL-X skeleton to cover only the existing limbs and employs "Residual-Limb Reconstruction" to explicitly remove distal mesh vertices and seal smooth residual surfaces. This reduces the residual limb 2D MPJPE from 73.61 px to 23.19 px (using HSMR backbone).

RHINO: Reconstructing Human Interactions with Novel Objects from Monocular Videos

From a single monocular RGB video with a moving perspective, RHINO reconstructs the "Human + Manipulated Unknown Object + Static Scene" into detailed 4D geometry within a unified world coordinate system. It leverages 3D foundation models to stabilize motion estimation for low-texture objects, decouples true object motion from "apparent motion" via camera motion subtraction, and performs joint optimization using per-component neural SDFs with a differentiable contact prior. It outperforms state-of-the-art baselines in both novel view synthesis and 4D reconstruction.

SketchFaceGS: Real-Time Sketch-Driven Face Editing and Generation with Gaussian Splatting

SketchFaceGS utilizes a feed-forward, coarse-to-fine architecture to map a single hand-drawn sketch (plus an optional reference image) to a real-time renderable photorealistic 3D Gaussian face in a single pass. By employing UV Mask Fusion and layer-wise feature fusion, it achieves free-view, optimization-free local real-time editing, outperforming SketchFaceNeRF in both generation fidelity (FID 92.65) and editing latency (~0.3s / 243 FPS).

Skullptor: High Fidelity 3D Head Reconstruction in Seconds with Multi-View Normal Prediction

Skullptor combines "data-driven multi-view normal prediction" and "inverse rendering mesh optimization" into a two-stage pipeline: first, a normal estimation model with cross-view attention predicts geometrically consistent surface normals from fewer than 10 sparse images; then, these normals are leveraged as strong geometric priors to optimize the mesh. Consequently, a 3D human head is reconstructed within 30 seconds using only 10 cameras, achieving quality comparable to traditional photogrammetry with dozens to hundreds of views while successfully recovering high-frequency details such as wrinkles and skin folds.

STAvatar: Soft Binding and Temporal Density Control for Monocular 3D Head Avatars Reconstruction

Ours proposes STAvatar, a framework for reconstructing high-fidelity, drivable 3D head avatars from monocular video. By utilizing a UV-adaptive soft binding framework and a temporal adaptive density control strategy, it significantly outperforms existing methods in handling occluded areas (e.g., mouth interior, eyelids) and fine details.

Tavatar: Topology-Aware Gaussian Attribute Derivation for Animatable Human Avatars

Tavatar no longer treats the rotation and scale of each 3D Gaussian as freely optimized parameters. Instead, it analytically derives them from the triangular geometry of the underlying deformable mesh. This anchors Gaussians naturally to the mesh topology, preventing them from detaching or creating holes under unseen complex poses (OOD). Normal error is reduced by 13.8% on X-Avatar and 17.9% on PeopleSnapshot compared to the best baseline, while maintaining competitive rendering quality.

TeHOR: Text-Guided 3D Human and Object Reconstruction with Textures

TeHOR utilizes text descriptions as semantic guidance to jointly optimize the geometry and texture of 3D humans and objects through Score Distillation Sampling from pre-trained diffusion models. It breaks the reliance of traditional methods on contact information, achieving accurate and semantically consistent 3D reconstruction for both contact-based and non-contact interactions.

Text-Driven 3D Hand Motion Generation from Sign Language Data

Utilizing large-scale sign language videos, sign language dictionaries, and LLMs, this work automatically constructs a dataset of 1.3 million "text-3D hand motion" pairs (BOBSL3DT). From this, the authors train HandMDM, a hand motion diffusion model driven by free-text descriptions (hand shape, position, finger/arm movement), which demonstrates strong generalization to unseen gestures, various sign languages, and non-sign language hand movements.

TokenHand: Discrete Token Representation for Efficient Hand Mesh Reconstruction

TokenHand encodes a 3D hand into \(M\) discrete tokens within a shared codebook and reframes "single-image hand mesh reconstruction" from a regression problem to a token classification problem. The classifier predicts the category of each token, while a pre-trained lightweight decoder restores the 778-vertex mesh without post-processing. It achieves a PA-MPJPE of 5.7mm at 65 FPS with only 3.0M parameters on FreiHAND.

UIKA: Fast Universal Head Avatar from Pose-Free Images

UIKA proposes a feed-forward drivable 3D Gaussian head avatar model. It projects an arbitrary number of "pose-free" input images (single image, multi-view, or mobile video) into a shared UV space via per-pixel face UV correspondence. A UV attention branch aggregates multi-view information to decode canonical space Gaussians, enabling reconstruction in a single forward pass and real-time driving at 220 FPS. It outperforms existing SOTA in both monocular and multi-view settings.

UniSH: Unifying Scene and Human Reconstruction in a Feed-Forward Pass

UniSH employs a feed-forward network to simultaneously output scene geometry, camera parameters, and metric-scale SMPL humans from monocular videos. By utilizing "expert depth model distillation + coarse-to-fine human-scene alignment," it transfers priors trained on synthetic data to real-world in-the-wild videos, achieving joint scene and human reconstruction in a single forward pass.

Unsupervised Monocular 3D Keypoint Discovery from Multi-View Diffusion Priors

KeyDiff3D treats pre-trained multi-view diffusion models as "sources of geometric priors"—using them both to generate multi-view images from a single image for self-supervision and to extract implicit 3D geometric cues from intermediate features to be lifted into explicit voxels. Consequently, it predicts accurate and generalizable 3D keypoints from a single image without any 3D annotations, camera parameters, or multi-view acquisition (achieving 119mm MPJPE on Human3.6M single-view, surpassing all single-view unsupervised baselines and matching some multi-view methods).

UST-Hand: An Uncertainty-aware Spatiotemporal Point Cloud Interaction Network for 3D Self-supervised Hand Pose Estimation

UST-Hand utilizes conditional normalizing flows to model 2D hand joints from each view as a probabilistic distribution rather than deterministic points. By sampling multiple hypotheses and triangulating them into a unified probabilistic 3D point cloud space, followed by iterative refinement using a Spatiotemporal Point Transformer (STPT), the model reduces the Mean Per Vertex Position Error (MPVPE) by up to 37.8% relative to previous SOTAs under self-supervised settings with noisy 2D pseudo-labels.

VIMCAN: Visual-Inertial 3D Human Pose Estimation with Hybrid Mamba-Cross-Attention Network

VIMCAN integrates Mamba's linear complexity for temporal modeling with Cross-Attention's cross-modal spatial reasoning into a hybrid architecture. By fusing RGB keypoints and wearable IMU data, it achieves 17.2 mm MPJPE on TotalCapture while supporting real-time inference at 60+ FPS on consumer-grade hardware.

Zero-Shot Reconstruction of Animatable 3D Avatars with Cloth Dynamics from a Single Image

DynaAvatar proposes the first zero-shot framework to reconstruct animatable 3D human avatars with motion-dependent cloth dynamics from a single image. By utilizing a static-to-dynamic knowledge transfer strategy and an optical flow-guided DynaFlow loss, it achieves realistic garment dynamic modeling under limited dynamic data, significantly outperforming existing methods.