🧑 Human Understanding

📹 ICCV2025 · 74 paper notes

📌 Same area in other venues: 🧠 NeurIPS2026 (9) · 🎞️ ECCV2026 (171) · 📷 CVPR2026 (218) · 🔬 ICLR2026 (55) · 🧪 ICML2026 (7) · 🤖 AAAI2026 (24)

🔥 Top topics: Face & Gaze ×13 · Human Pose ×11 · Avatars ×7 · Diffusion Models ×6 · Speech & Audio ×5

GENMO: A GENeralist Model for Human MOtion

This paper proposes GENMO, the first generalist model that unifies human motion estimation (recovering motion from video/2D keypoints) and motion generation (synthesizing motion from text/music/keyframes) within a single framework. Through a dual-mode training paradigm (regression + diffusion), GENMO achieves both precise estimation and diverse generation in a single model.

Avat3r: Large Animatable Gaussian Reconstruction Model for High-fidelity 3D Head Avatars

This paper presents Avat3r — the first animatable large reconstruction model (LRM) that regresses high-quality drivable 3D Gaussian head avatars from only 4 input images in a feed-forward manner. By integrating DUSt3R positional maps and Sapiens semantic features as priors, and modeling expression-driven animation via simple cross-attention, Avat3r substantially outperforms existing methods on the Ava256 and NeRSemble datasets.

HUMOTO: A 4D Dataset of Mocap Human Object Interactions

This paper presents HUMOTO, a high-fidelity 4D human-object interaction dataset comprising 735 sequences (7,875 seconds at 30fps), covering 63 precisely modeled objects with 72 articulated parts. It introduces an LLM-driven scene scripting pipeline and a multi-sensor capture system, achieving significantly superior hand pose accuracy and interaction quality compared to existing datasets.

DreamActor-M1: Holistic, Expressive and Robust Human Image Animation with Hybrid Guidance

DreamActor-M1 proposes a human image animation framework based on the DiT architecture, achieving fine-grained facial and body control through hybrid control signals comprising implicit facial representations, 3D head spheres, and 3D body skeletons. Combined with complementary appearance guidance and a progressive training strategy, the framework supports multi-scale generation ranging from portrait to full-body.

KinMo: Kinematic-Aware Human Motion Understanding and Generation

This paper proposes the KinMo framework, which decomposes human motion into six kinematic groups and their interactions as a hierarchically describable representation. An automatic annotation pipeline generates fine-grained textual descriptions at multiple granularities. Combined with hierarchical text-motion alignment (HTMA) and a coarse-to-fine motion generation strategy, KinMo significantly improves motion understanding and fine-grained motion generation.

Weakly Supervised Visible-Infrared Person Re-Identification via Heterogeneous Expert Collaborative Consistency Learning

This paper proposes the first weakly supervised paradigm for visible-infrared person re-identification (VIReID), which relies solely on intra-modality identity annotations (without cross-modal correspondence labels). A heterogeneous expert collaborative consistency learning framework is introduced to establish cross-modal identity correspondences, achieving performance close to fully supervised methods.

SynFER: Towards Boosting Facial Expression Recognition with Synthetic Data

This paper proposes SynFER, a diffusion-model-based facial expression synthesis framework that achieves fine-grained expression generation via dual control signals — text descriptions and Facial Action Units (FAUs) — and introduces a FERAnno label calibrator to ensure annotation reliability. The effectiveness of synthetic data for FER is validated across four learning paradigms: self-supervised, supervised, zero-shot, and few-shot learning.

IDFace: Face Template Protection for Efficient and Secure Identification

This paper proposes IDFace, a homomorphic encryption (HE)-based face template protection method that achieves retrieval over 1 million encrypted templates in only 126ms — incurring merely a 2× overhead compared to unprotected retrieval — through two key techniques: a near-isometric transformation (real-valued vector → ternary vector) and a space-efficient encoding scheme.

CleanPose: Category-Level Object Pose Estimation via Causal Learning and Knowledge Distillation

This work is the first to introduce causal reasoning into category-level object pose estimation (COPE). It eliminates spurious correlations induced by data bias via a front-door adjustment-based causal reasoning module, and provides unbiased categorical semantic supervision through residual knowledge distillation from the 3D foundation model ULIP-2. The method achieves 61.7% on the strict 5°2cm metric on REAL275, surpassing the state of the art by 4.7%.

Dynamic Reconstruction of Hand-Object Interaction with Distributed Force-aware Contact Representation

This paper proposes ViTaM-D, a vision-tactile fusion framework that achieves dynamic reconstruction of hand-object interaction for both rigid and deformable objects. The framework introduces a novel Distributed Force-aware Contact Representation (DF-Field) and a two-stage pipeline consisting of visual dynamic tracking followed by force-aware optimization. The HOT dataset is also introduced to address the evaluation gap in deformable object hand-object interaction.

GenM3: Generative Pretrained Multi-path Motion Model for Text Conditional Human Motion Generation

This paper proposes GenM3, a framework that learns unified discrete motion representations via a Multi-Expert VQ-VAE (MEVQ-VAE) and employs a Multi-path Motion Transformer (MMT) to handle intra-modal variation and cross-modal alignment. By integrating 11 motion datasets (~220 hours), GenM3 achieves state-of-the-art FID of 0.035 on HumanML3D.

DADM: Dual Alignment of Domain and Modality for Face Anti-Spoofing

This paper proposes the DADM framework, which simultaneously addresses intra-domain modality misalignment and inter-domain modality misalignment in multimodal face anti-spoofing via a Mutual Information Mask (MIM) module and a dual domain-modality alignment optimization strategy, achieving state-of-the-art performance across four evaluation protocols.

GestureHYDRA: Semantic Co-speech Gesture Synthesis via Hybrid Modality Diffusion Transformer and Cascaded-Synchronized Retrieval-Augmented Generation

This paper proposes GestureHYDRA, a co-speech gesture synthesis system based on a Hybrid-Modality Diffusion Transformer and Cascaded-Synchronized Retrieval-Augmented Generation, capable of reliably activating semantically explicit gestures such as numerical and directional indications.

MDD: A Dataset for Text-and-Music Conditioned Duet Dance Generation

This paper introduces the Multimodal DuetDance (MDD) dataset — the first large-scale, professional-grade duet dance dataset simultaneously integrating motion, music, and text descriptions. MDD comprises 620 minutes of motion capture data spanning 15 dance styles and over 10K fine-grained text annotations, and defines two new tasks: Text-to-Duet and Text-to-Dance Accompaniment.

Bi-Level Optimization for Self-Supervised AI-Generated Face Detection

This paper proposes BLADES, a method that employs bi-level optimization to explicitly align self-supervised pretraining with the AI-generated face detection objective. The inner loop optimizes a visual encoder on pretext tasks including EXIF classification/ranking and face manipulation detection, while the outer loop optimizes task weights to improve performance on a proxy detection task, enabling cross-generator generalization without relying on any synthetic face data.

Multi-view Gaze Target Estimation

This paper is the first to extend Gaze Target Estimation (GTE) from single-view to multi-view settings. By integrating three modules — Head Information Aggregation (HIA), Uncertainty-based Gaze Selection (UGS), and Epipolar-based Scene Attention (ESA) — the method fuses information across multiple cameras. It significantly outperforms single-view state-of-the-art methods on the newly introduced MVGT dataset and enables cross-view estimation that single-view methods cannot handle.

AR-VRM: Imitating Human Motions for Visual Robot Manipulation with Analogical Reasoning

This paper proposes AR-VRM, the first method to enhance visual robot manipulation (VRM) through explicit imitation of human hand keypoints. It employs a keypoint vision-language model pretrained on large-scale human activity videos to acquire motion knowledge, and establishes correspondences between human hand keypoints and robot components via analogical reasoning.

MagShield: Towards Better Robustness in Sparse Inertial Motion Capture Under Magnetic Disturbances

MagShield is proposed as the first method addressing magnetic disturbance in sparse inertial motion capture systems. It adopts a two-stage detect-then-correct strategy: detecting magnetic disturbances via joint analysis of multiple IMUs, and correcting orientation errors using a human motion prior network. The approach can be plug-and-played into existing sparse IMU motion capture systems to enhance robustness.

One-Shot Knowledge Transfer for Scalable Person Re-Identification

This paper proposes OSKT (One-Shot Knowledge Transfer), which distills teacher model knowledge into a compact intermediate representation termed a "weight chain," enabling the generation of student models of arbitrary sizes for person re-identification with a single round of computation.

CarGait: Cross-Attention based Re-ranking for Gait Recognition

This paper proposes CarGait, a cross-attention based re-ranking method for gait recognition. By performing strip-wise cross-attention between probe and candidate sequences, CarGait learns fine-grained gait correspondences and maps global features from pretrained single-stage models into a new discriminative embedding space. The method consistently improves Rank-1/5 accuracy across seven gait models on three major benchmarks: Gait3D, GREW, and OU-MVLP.

GGTalker: Talking Head Synthesis with Generalizable Gaussian Priors and Identity-Specific Adaptation

GGTalker proposes a prior-adaptation two-stage training strategy that learns generalizable audio-to-expression and expression-to-visual priors from large-scale datasets, then rapidly adapts to a specific identity. The method achieves state-of-the-art performance across rendering quality, 3D consistency, lip synchronization, and training efficiency, requiring only 20 minutes of adaptation to generate photorealistic talking-head videos at 120 FPS.

High-Resolution Spatiotemporal Modeling with Global-Local State Space Models for Video-Based Human Pose Estimation

This paper proposes GLSMamba, the first pure-Mamba framework for video-based human pose estimation (VHPE). It models global dynamic context via a Global Spatiotemporal Mamba (GSM) module—featuring 6D selective space-time scanning and spatiotemporal-modulated scan merging—and captures local keypoint details via a Local Refinement Mamba (LRM) with windowed spatiotemporal scanning. The method achieves state-of-the-art performance on four benchmarks with linear computational complexity.

ImHead: A Large-scale Implicit Morphable Model for Localized Head Modeling

imHead proposes the first large-scale implicit 3D head morphable model. Through a global-local decoupled architecture trained on a dataset of 4,000 identities, it achieves both a compact implicit representation and localized facial editing, surpassing existing methods in reconstruction accuracy and editing flexibility.

Controllable and Expressive One-Shot Video Head Swapping

This paper proposes a diffusion-based multi-condition controllable video head swapping framework (SwapAnyHead) that achieves high-fidelity identity preservation, seamless background blending, and accurate cross-identity expression transfer and editing via a shape-agnostic mask strategy, a hair enhancement strategy, and an expression-aware 3DMM-driven landmark retargeting module.

Monocular Facial Appearance Capture in the Wild

This paper proposes a method for reconstructing facial appearance attributes (diffuse albedo, specular intensity, specular roughness) from monocular head-rotation videos. By introducing an occlusion-aware split-sum approximation shading model, the method achieves studio-grade facial appearance capture quality without imposing any simplifying assumptions on the illumination environment.

DynFaceRestore: Balancing Fidelity and Quality in Diffusion-Guided Blind Face Restoration

This paper proposes DynFaceRestore, which reformulates blind degradation as a Gaussian deblurring problem via Dynamic Blur Level Mapping (DBLM), and achieves an optimal fidelity-quality trade-off during diffusion model sampling through a Dynamic Starting Step lookup table (DSST) and a Dynamic Guidance Scaling Adjuster (DGSA).

NGD: Neural Gradient Based Deformation for Monocular Garment Reconstruction

This paper proposes NGD, a neural gradient-based deformation method that decomposes the Jacobian field into a frame-invariant static component and a frame-dependent dynamic component. Combined with an adaptive remeshing strategy, NGD reconstructs high-fidelity dynamic garment geometry and appearance from monocular video, significantly outperforming existing SOTA methods on challenging scenarios such as loose-fitting garments.

PoseSyn: Synthesizing Diverse 3D Pose Data from In-the-Wild 2D Data

This paper proposes the PoseSyn framework, which identifies hard samples for a target pose estimator (TPE) from in-the-wild 2D pose data via an Error Extraction Module (EEM), then expands inaccurate pseudo-labels into diverse motion sequences via a Motion Synthesis Module (MSM). A human animation model subsequently renders these sequences into realistic training images with accurate 3D annotations, improving 3D pose estimation accuracy by up to 14% across multiple real-world benchmarks.

Signs as Tokens: A Retrieval-Enhanced Multilingual Sign Language Generator

This paper proposes SOKE, a multilingual sign language generation framework built upon pretrained language models. It discretizes continuous sign language motion into token sequences via a decoupled tokenizer, and achieves high-quality text-to-3D-avatar sign language generation across multiple languages through multi-head decoding and retrieval-augmented strategies.

UDC-VIT: A Real-World Video Dataset for Under-Display Cameras

This paper presents UDC-VIT, the first real-world video dataset for under-display cameras (UDC), comprising 647 video clips with 116,460 frames in total. A carefully designed dual-camera beam-splitter acquisition system achieves precise spatiotemporal alignment. With face recognition as the primary application scenario, the dataset reveals the inadequacy of synthetic datasets in simulating real-world UDC degradation.

Contact-Aware Refinement of Human Pose Pseudo-Ground Truth via Bioimpedance Sensing

This paper proposes BioTUCH, a framework that detects self-contact events via wrist-to-wrist bioimpedance sensing and performs contact-aware 3D arm pose refinement in conjunction with a visual pose estimator, achieving an average improvement of 11.7% in reconstruction accuracy.

EgoAgent: A Joint Predictive Agent Model in Egocentric Worlds

This paper proposes EgoAgent, a unified predictive agent model that simultaneously learns to represent egocentric visual observations, predict future world states, and generate 3D human motions within a single Transformer.

HccePose(BF): Predicting Front & Back Surfaces to Construct Ultra-Dense 2D-3D Correspondences for Pose Estimation

This paper proposes simultaneously predicting the 3D coordinates of both the front and back surfaces of an object and densely sampling between the two surfaces to construct ultra-dense 2D-3D correspondences. Combined with a novel Hierarchical Continuous Coordinate Encoding (HCCE), the method surpasses existing state-of-the-art approaches on all seven core BOP benchmark datasets.

LVFace: Progressive Cluster Optimization for Large Vision Models in Face Recognition

This paper proposes LVFace, which addresses training instability of ViT in large-scale face recognition via a Progressive Cluster Optimization (PCO) strategy. The training process is decomposed into three stages — feature alignment, centroid stabilization, and boundary refinement — achieving state-of-the-art results on multiple benchmarks.

MixRI: Mixing Features of Reference Images for Novel Object Pose Estimation

This paper proposes MixRI, a lightweight network with only 12 reference images and 5.3M parameters, which establishes 2D–3D correspondences between multiple reference images and a query image via a multi-view feature fusion strategy. MixRI achieves pose estimation performance comparable to methods requiring hundreds of reference images across 7 core BOP challenge datasets.

OpenAnimals: Revisiting Person Re-Identification for Animals Towards Better Generalization

This paper develops the OpenAnimals open-source framework, systematically revisiting the transferability of person re-identification methods to animal re-identification. It proposes ARBase, an animal-oriented strong baseline that substantially outperforms existing person ReID methods across multiple benchmarks.

RayPose: Ray Bundling Diffusion for Template Views in Unseen 6D Object Pose Estimation

This work reformulates unseen 6D object pose estimation as a ray alignment problem, proposes an object-centric ray parameterization scheme, and employs a diffusion transformer to infer the 6D pose of a query image from multiple template images with known poses.

SemGes: Semantics-aware Co-Speech Gesture Generation using Semantic Coherence and Relevance Learning

SemGes proposes a two-stage framework that integrates semantic information at both global and fine-grained levels through semantic coherence and semantic relevance learning, generating co-speech gestures aligned with speech semantics. The method surpasses existing approaches on two benchmarks: BEAT and TED-Expressive.

SemTalk: Holistic Co-speech Motion Generation with Frame-level Semantic Emphasis

SemTalk decomposes co-speech motion into rhythm-aligned base motions and semantics-aware sparse motions, and adaptively fuses them via learned semantic scores to achieve high-quality holistic co-speech motion generation with frame-level semantic emphasis.

Sequential Keypoint Density Estimator: An Overlooked Baseline of Skeleton-Based Video Anomaly Detection

SeeKer proposes to autoregressively factorize the joint density of skeleton sequences at the keypoint level, detecting abnormal human behaviors by predicting conditional Gaussian distributions over subsequent keypoints. It substantially outperforms existing methods on the UBnormal and MSAD-HR benchmarks.

What's Making That Sound Right Now? Video-centric Audio-Visual Localization

This paper proposes AVATAR, a video-level audio-visual localization benchmark, and TAVLO, a temporally-aware model that addresses the neglect of temporal dynamics in conventional AVL methods through high-resolution temporal modeling.

AdaHuman: Animatable Detailed 3D Human Generation with Compositional Multiview Diffusion

AdaHuman is proposed as a framework that generates high-fidelity, animatable 3D human avatars from a single image via a pose-conditioned 3D joint diffusion model and a compositional 3DGS refinement module.

AJAHR: Amputated Joint Aware 3D Human Mesh Recovery

The first 3D human mesh recovery framework for amputees — by synthesizing 1M+ amputee images (A3D), designing the BPAC-Net amputation classifier to distinguish amputation from occlusion, and employing a dual-tokenizer switching strategy to encode amputation/normal pose priors separately. The method achieves substantial improvements on amputee data (MVE 16.87 lower than TokenHMR on ITW-amputee) while remaining competitive on non-amputee benchmarks.

ATLAS: Decoupling Skeletal and Shape Parameters for Expressive Parametric Human Modeling

This paper presents ATLAS, a parametric human body model that explicitly decouples external surface shape from internal skeletal parameters, incorporates sparse nonlinear pose correctives, and is trained on 600K high-resolution scans, achieving more accurate and controllable human body modeling than SMPL-X.

Bring Your Rear Cameras for Egocentric 3D Human Pose Estimation

This paper is the first to investigate the value of rear-mounted cameras on HMDs for egocentric 3D whole-body pose estimation. It proposes a Transformer-based multi-view heatmap refinement method with an uncertainty-aware masking mechanism, achieving >10% MPJPE improvement on the newly constructed Ego4View dataset.

Contact-Aware Amodal Completion for Human-Object Interaction via Multi-Regional Inpainting

This paper proposes the first amodal completion framework tailored for human-object interaction (HOI) scenes. It leverages human body topology and contact information to identify occluded regions via convex hull operations, and employs a multi-regional inpainting strategy on a pretrained diffusion model to achieve high-quality occluded object completion without any additional training.

DAViD: Data-efficient and Accurate Vision Models from Synthetic Data

This work demonstrates that high-fidelity procedural synthetic data suffices to train human-centric dense prediction models that match the accuracy of foundation models such as Sapiens-2B, requiring only 300K synthetic images, 0.3B parameters, and less than 1/16 the training cost of comparable approaches, while achieving state-of-the-art or near-SOTA performance on depth estimation, surface normal estimation, and soft foreground segmentation.

ETCH: Generalizing Body Fitting to Clothed Humans via Equivariant Tightness

This paper proposes ETCH, a framework that models SE(3)-equivariant tightness vectors from clothing surfaces to body surfaces, reducing clothed human body fitting to a tightness-aware sparse marker fitting task. On the CAPE and 4D-Dress datasets, ETCH achieves 16.7%–69.5% reduction in joint error on loose garments and an average 49.9% improvement in shape accuracy compared to state-of-the-art methods (both tightness-agnostic and tightness-aware).

Event-Driven Storytelling with Multiple Lifelike Humans in a 3D Scene

This paper proposes an event-driven LLM framework that decomposes multi-character behavior planning in 3D scenes into two modules — a Narrator for event-by-event generation and an Event Parser for fine-grained spatial reasoning — achieving, for the first time, long-horizon natural interaction motion generation for 4–5+ characters in large-scale multi-room 3D scenes.

FaceLift: Learning Generalizable Single Image 3D Face Reconstruction from Synthetic Heads

This paper presents FaceLift, a single-image 360° high-quality 3D human head reconstruction method trained exclusively on synthetic data yet generalizing well to real-world images. It generates identity-consistent multi-view images via a multi-view latent diffusion model, then feeds them into a Transformer-based reconstructor to produce pixel-aligned 3D Gaussian representations.

Fish2Mesh Transformer: 3D Human Mesh Recovery from Egocentric Vision

This paper proposes Fish2Mesh, a fisheye-aware Transformer model that embeds the spherical geometry of fisheye images into a Swin Transformer via an Egocentric Positional Encoding (EPE) based on equirectangular projection, enabling accurate 3D human mesh recovery from a head-mounted fisheye camera in egocentric perspective.

GAS: Generative Avatar Synthesis from a Single Image

GAS is a framework that unifies novel view synthesis and novel pose synthesis into a video generation task by combining dense appearance cues from a generalizable NeRF with a video diffusion model. A modality switcher decouples the two tasks, enabling view-consistent and temporally coherent human avatar generation from a single image.

GazeGaussian: High-Fidelity Gaze Redirection with 3D Gaussian Splatting

This paper proposes GazeGaussian, the first high-fidelity gaze redirection method based on 3D Gaussian Splatting (3DGS). By employing a dual-stream 3DGS model to separately represent the facial and eye regions, the method introduces an explicit Gaussian eyeball rotation representation and an expression-guided neural renderer (EGNR), achieving state-of-the-art performance in gaze accuracy, synthesis quality, and rendering speed.

GUAVA: Generalizable Upper Body 3D Gaussian Avatar

This paper presents GUAVA, the first framework for feed-forward reconstruction of animatable upper-body 3D Gaussian avatars from a single image. By combining template Gaussians and UV Gaussians in a canonical space representation, GUAVA supports rich facial expression and gesture driving, completing reconstruction in approximately 0.1 s with real-time rendering capability.

HairCUP: Hair Compositional Universal Prior for 3D Gaussian Avatars

This paper proposes HairCUP, a compositional universal prior model that decomposes head modeling into two independent latent spaces for face and hair. By leveraging a synthetic hairless data creation pipeline for effective disentanglement, the model supports flexible face/hairstyle swapping and few-shot monocular adaptation.

HIS-GPT: Towards 3D Human-In-Scene Multimodal Understanding

This paper proposes the HIS-QA task, the HIS-Bench benchmark, and HIS-GPT — the first foundation model for joint 3D human-in-scene understanding. Through an Auxiliary Interaction Module (AInt) and a Layout-Trajectory Positional Encoding (LTP), HIS-GPT captures fine-grained human–scene interactions and substantially outperforms GPT-4o and other baselines across 16 sub-tasks.

HumanOLAT: A Large-Scale Dataset for Full-Body Human Relighting and Novel-View Synthesis

This paper introduces HumanOLAT — the first publicly available large-scale full-body multi-view OLAT (One-Light-at-a-Time) dataset, comprising 21 subjects × 3 poses × 40 viewpoints × 344 lighting conditions ≈ 850K frames, providing a high-quality benchmark for human relighting and novel-view synthesis.

Identity Preserving 3D Head Stylization with Multiview Score Distillation

This paper proposes a 3D head stylization framework based on Likelihood Distillation (LD), achieving high-quality stylization with identity preservation under 360-degree consistent rendering through multiview grid scoring, mirror gradients, and rank-weighted score tensors.

MaskHand: Generative Masked Modeling for Robust Hand Mesh Reconstruction in the Wild

This paper proposes MaskHand, the first method to introduce generative masked modeling into 3D hand mesh reconstruction. It discretizes continuous hand poses into tokens via VQ-MANO, then employs a context-guided masked Transformer to learn the probability distribution of 2D-to-3D mappings. During inference, confidence-guided iterative sampling is used to generate high-precision hand meshes, achieving a 19.5% reduction in PA-MPJPE on the HO3Dv3 zero-shot evaluation.

MemoryTalker: Personalized Speech-Driven 3D Facial Animation via Audio-Guided Stylization

This paper proposes MemoryTalker, a two-stage training framework (Memorizing + Animating) that employs a key-value memory network to store generic facial motions and generates personalized 3D facial animations driven solely by audio via audio-guided stylized memory retrieval, requiring no additional prior information at inference time.

MeshMamba: State Space Models for Articulated 3D Mesh Generation and Reconstruction

MeshMamba introduces a Mamba state space model-based approach for articulated 3D mesh generation and reconstruction. By designing vertex serialization strategies based on body-part UV maps and template mesh coordinates, the method achieves efficient generation and reconstruction of meshes with tens of thousands of vertices, running 6–9× faster than Transformer-based counterparts.

MoGA: 3D Generative Avatar Prior for Monocular Gaussian Avatar Reconstruction

MoGA is proposed to reconstruct high-fidelity 3D Gaussian avatars from a single image by learning a generative 3D avatar prior and leveraging it as a strong constraint for initialization, regularization, and pose optimization, substantially outperforming existing methods.

PersPose: 3D Human Pose Estimation with Perspective Encoding and Perspective Rotation

This paper proposes the PersPose framework, which addresses the inaccurate depth estimation caused by existing methods neglecting field-of-view (FOV) information. It encodes cropped camera intrinsics as a 2D map via Perspective Encoding (PE) and centers the subject through Perspective Rotation (PR) to eliminate perspective distortion.

PHD: Personalized 3D Human Body Fitting with Point Diffusion

This paper proposes PHD, a personalized 3D human pose estimation paradigm that first calibrates user-specific body shape via SHAPify, then employs a shape-conditioned point diffusion model (PointDiT) as a 3D prior, and iteratively optimizes pose parameters through Point Distillation Sampling combined with 2D keypoint constraints, achieving state-of-the-art absolute pose accuracy on the EMDB dataset.

RapVerse: Coherent Vocals and Whole-Body Motion Generation from Text

This work constructs the large-scale rap dataset RapVerse and proposes a unified autoregressive transformer framework that, for the first time, simultaneously generates coherent singing vocals and whole-body 3D motion from lyric text.

SceneMI: Motion In-betweening for Modeling Human-Scene Interactions

This work formally introduces the scene-aware motion in-betweening problem and proposes the SceneMI framework, which comprehensively encodes scene context via a dual-layer scene descriptor (global voxels + local BPS). By leveraging the denoising capability of diffusion models to handle noisy keyframes, SceneMI reduces the collision frame rate by 56.9% on TRUMANS, and reduces foot skating by 37.5% and jitter by 56.5% on the real-world GIMO dataset.

Sequential Gaussian Avatars with Hierarchical Motion Context

This paper proposes SeqAvatar, which leverages explicit 3DGS representations combined with hierarchical motion context (coarse-grained skeletal motion + fine-grained per-point velocity) to model motion-correlated appearance changes in human avatars. Spatio-temporal multi-scale sampling further enhances the robustness of motion conditioning. SeqAvatar achieves state-of-the-art rendering quality across multiple datasets while maintaining real-time rendering speed.

SHeaP: Self-Supervised Head Geometry Predictor Learned via 2D Gaussians

SHeaP replaces traditional differentiable mesh rendering with 2D Gaussian Splatting for self-supervised 3DMM prediction training. By binding Gaussians to the 3DMM mesh for re-animation, and introducing a graph-convolution-based Gaussian regressor together with geometry consistency regularization, SHeaP surpasses all self-supervised methods on the NoW and Nersemble benchmarks.

SpinMeRound: Consistent Multi-View Identity Generation Using Diffusion Models

This paper presents SpinMeRound, an identity-conditioned multi-view diffusion model that generates 360° full-head portraits with consistent identity and corresponding normal maps from a single or few face images, surpassing existing multi-view diffusion methods on face novel view synthesis benchmarks.

StrandHead: Text to Hair-Disentangled 3D Head Avatars Using Human-Centric Priors

This paper presents StrandHead, the first framework for generating strand-level 3D head avatars by distilling human-centric 2D diffusion priors. It introduces a differentiable prismatization algorithm to convert hair strands into watertight meshes with gradient backpropagation, and designs regularization losses based on statistical geometric priors of hair strands to ensure hairstyle realism.

SVG-Head: Hybrid Surface-Volumetric Gaussians for High-Fidelity Head Reconstruction and Real-Time Editing

SVG-Head is proposed as a hybrid representation combining surface Gaussians (with explicit texture maps) and volumetric Gaussians (for supplementary modeling of non-Lambertian regions), achieving, for the first time, real-time appearance editing of high-fidelity Gaussian head avatars.

UniEgoMotion: A Unified Model for Egocentric Motion Reconstruction, Forecasting, and Generation

This paper proposes UniEgoMotion, the first unified egocentric motion model that achieves 3D human motion reconstruction, forecasting, and generation from an egocentric perspective within a single model, via a conditional motion diffusion framework and a head-centric motion representation. The large-scale EE4D-Motion dataset is also released.

VoluMe: Authentic 3D Video Calls from Live Gaussian Splat Prediction

Microsoft proposes the first method for real-time prediction of 3D Gaussian Splatting reconstructions from a monocular 2D camera, simultaneously satisfying four requirements: authenticity, realism, liveness, and temporal stability. This enables anyone to conduct volumetric 3D video calls using only a standard laptop camera.

VolumetricSMPL: A Neural Volumetric Body Model for Efficient Interactions, Contacts, and Collisions

This paper proposes VolumetricSMPL, an efficient neural volumetric body model based on Neural Blend Weights (NBW), achieving 10× inference speedup and 6× memory reduction over its predecessor COAP, while providing more accurate differentiable collision modeling through SDF (rather than occupancy function) representation.