🚗 Autonomous Driving

🎞️ ECCV2026 · 52 paper notes

📌 Same area in other venues: 📷 CVPR2026 (157) · 🔬 ICLR2026 (50) · 🧪 ICML2026 (8) · 🤖 AAAI2026 (56) · 🧠 NeurIPS2025 (47) · 📹 ICCV2025 (91)

🔥 Top topics: Autonomous Driving ×13 · Agents ×5 · 3D Object Detection ×4 · Segmentation ×2 · 3D Gaussian Splatting ×2

2K Retrofit: Entropy-Guided Efficient Sparse Refinement for High-Resolution 3D Geometry Prediction

This paper proposes 2K Retrofit, a universal framework enabling existing 3D foundation models (e.g., Depth Anything, VGGT) to output 2K resolution geometry predictions without modifying or retraining the base models. The core idea is to perform fast coarse prediction with a frozen base model and apply full-resolution correction only to high-uncertainty pixel regions using an entropy-guided sparse refinement mechanism, achieving or exceeding the accuracy of full-resolution methods while significantly reducing computational and GPU memory overhead.

A Physics-Grounded Benchmark for Multi-Agent Dynamics in World Models

CrashTwin combines traffic-interaction data, calibration-free reconstruction, and separate physical diagnostics to show that visually coherent world-model videos can still contain incorrect momentum exchange and kinetic-energy increases; its contribution is an evaluation framework, not a new video generator.

Adaptive Neural Dynamics for Robust Geometric LiDAR-Inertial State Estimation on UAVs

NeuroSDE-LIO retains the geometric estimation backbone of FAST-LIO2, adaptively combines linear bias priors with neural stochastic bias dynamics, and propagates their uncertainty into the filter, reducing UAV-Lab absolute pose error from 2.13 m to 1.34 m at a LiDAR processing rate of 19.2 Hz.

Agent-OBJ: Prompt-Driven 3D Adversaries for Multi-Modal Perception

Agent-OBJ searches language prompts controlling the shape and appearance of a 3D object using only detection boxes and confidence scores, combining naturalness and cross-view stability constraints to achieve 64.0% joint disappearance from camera and fusion detectors in simulated nuScenes insertion experiments.

AiSCREAM: Absolute Target Localization with Language-Conditioned Cross-View Alignment for Autonomous Vehicles

AiSCREAM uses an aerial image generated from a single front view as an auxiliary spatial cue, aligns the two views through language-conditioned semantic segmentation, and combines multitask localization with MLLM refinement to predict a metric target and acceptable region, reducing DRAMATiST RMSE from the strongest model baseline's 8.56 m to 5.00 m.

Analytic Bayesian Uncertainty for LiDAR Segmentation: A Single-pass Generative Approach

GMM-NIG places conjugate Bayesian posteriors over the means and variances of a class-conditional Gaussian mixture head attached to a deterministic segmentation backbone, then uses analytic Student-t prediction to reach 65.71% mIoU, 2.01% ACE, and 53.33% failure-detection AUPR with RangeFormer on SemanticKITTI without repeated stochastic forward passes.

ARC-Loc: Leveraging Azimuthal Ray Convergence as a Geometric Cue for Direct Cross-View Localization

Under a known heading or a bounded-noise heading prior, ARC-Loc converts ground-satellite matches into azimuthal lines and estimates position through differentiable weighted intersection and RANSAC, requiring neither depth estimation nor BEV transformation and reaching 2.52 m mean error on VIGOR Same-Area with known orientation, while remaining limited by narrow-view cross-area generalization.

ASSCG: Just-Right Gating over Chattering for Fast–Slow LLM Planning in Autonomous Driving

ASSCG learns not only when to query a slow language model but also when to reuse or suppress its cached guidance, using temporal gating and compute-aware reinforcement learning to raise AsyncDriver's nuPlan Hard20 score from 65.00 to 67.28 while reducing average end-to-end inference latency from 0.80 to 0.32 seconds per frame.

ASTAD: Asymmetric Style Transfer for Synthetic-to-Real Domain Adaptation in Autonomous Driving

Addressing the asymmetric constraint in autonomous driving scenarios where "synthetic images possess perfect annotations, whereas real-world style reference images are unannotated," ASTModel proposes a two-stage training-free framework. It first extracts coarse semantic priors from unlabeled style images via DINO prototype matching. During the reverse diffusion process, these priors are refined through multi-layer semantic voting, followed by class-consistent style injection enabled by robust median-threshold filtering and pixel-proportion modulated hybrid AdaIN. This generates realistic target-domain style data while preserving semantic structures, achieving a 3.2x speedup.

AutoWeather4D: Autonomous Driving Video Weather Conversion via G-Buffer Dual-Pass Editing

AutoWeather4D extracts metric geometry and materials from driving videos, edits weather interactions and light transport separately, and uses constrained video diffusion to improve sensor realism, reaching a 0.2586 CLIP score and 0.915 vehicle detection IoU on Waymo; its main advantage is spatially grounded, parametric weather and lighting control rather than a large numerical lead over generative baselines.

AV2T-Gen: Aerial Visible to Thermal Generation with Environment and Vehicle State Guidance

AV2T-Gen adds environmental prompts and motion-aware object conditions to Instruct-Pix2Pix-style diffusion so that aerial RGB-to-thermal translation goes beyond appearance transfer, reducing ThermalGen's FID from 83.16 to 67.45 in joint four-scene evaluation while improving vehicle detection on synthetic thermal images.

BEV-GS: Feed-forward Gaussian Splatting in Bird's-Eye-View for Road Reconstruction

BEV-GS replaces perspective-pixel Gaussian prediction with elevation and texture prediction on a regular BEV grid, achieving 1.73 cm average absolute elevation error, 28.36 dB novel-view PSNR, and 26.3 FPS parameter prediction on RSRD; single-frame refers to inference input, while training still uses elevation labels and stereo-image supervision.

BEVLM: Distilling Semantic Knowledge from LLMs into Bird's-Eye View Representations

BEVLM first establishes that fused BEV tokens support stronger spatial reasoning in language models, then trains the BEV encoder with frozen-LLM question-answering gradients and detection supervision, raising UniAD's NeuroNCAP score from 2.38 to 3.05 and reducing collision rate from 0.56 to 0.47 without introducing an LLM into driving-time inference.

BEVOpen3D: Towards Open-World 3D Object Detection in Bird's-Eye-View

BEVOpen3D teaches a vision model to correct rule-generated 3D boxes using seen-class supervision, then trains a LiDAR-only student through triple-source pseudo-label fusion and BEV heatmap distillation, reaching 30.10 mAP and 16.26 unseen AP on the nuScenes seven-novel-class split, gains of 0.73 and 1.33 percentage points over Find n' Propagate.

Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives

BeyondDrive trains a planner to approach expert trajectories while avoiding generated trajectories that are nearby but unsafe, improving optimized LTF from 88.7 to 89.7 PDMS on NAVSIM v1 and from 89.1 to 90.1 EPDMS on NAVSIM v2, with additional evidence from other planners and zero-shot HUGSIM evaluation.

BeyondSight: Object Permanence for End-to-End Autonomous Driving

BeyondSight separates historical actor propagation from current-image updates in SparseDrive and trains with annotations spanning unobservable intervals, keeping fully occluded actors available to prediction and planning while raising unobservable-actor mAP from 0 to 0.249 and reducing standard nuScenes average open-loop planning L2 from 0.61 to 0.54.

BitRIC: Efficient Neural Compression of LiDAR Range Images via Hierarchical Bitplanes

BitRIC decomposes integer range images into binary planes ordered from most to least significant, entropy-codes them using shared context and three-stage probability estimation, and compensates for truncated low bits through one-step residual refinement, achieving D1 BD-BR of −61.73% against JPEG2000 and lossless coding at 6.05 Bpp on WOD while retaining strong downstream detection accuracy.

Boba: Batched Simulation for Physics-Based Gaussian Digital Twins

Boba jointly optimizes the physical model, skinning, and GPU execution of PhysTwin-based deformable Gaussian digital twins, reducing single-instance Orin latency from 330.9 ms to 32.5 ms and reaching 3309.9 aggregate FPS on RTX 4090 while retaining reconstruction and prediction quality close to the baseline.

CaRe: Critical Parameter Rectification for Efficient Visual Modeling

CaRe expresses a standard weight as a dense base plus a learned Softplus-gated correction, concentrating training-time corrections on important entries and merging them into an ordinary layer before deployment; CaReNet-M1 achieves 73.6% ImageNet-1K Top-1 at 33,203 images/s on an A100, outperforming EfficientViM-M1 by 0.7 percentage points.

CausalDrive: Real-time Causal World Models for Autonomous Driving

CausalDrive generates reactive driving videos from only an initial front-view image, an ego trajectory, and a global interaction prompt; a causal autoregressive teacher and context-forced DMD reduce inference to 1-4 steps, reaching 12.4 FPS on one A100 and an 82.0% yielding rate under the Polite condition on SocioDrive-Bench, without eliminating all false collisions.

CCFM: Collision-Constrained Flow Matching for Safety-Critical Scenario Generation

CCFM reformulates safety-critical driving scenario generation as flow matching sampling with hard collision constraints: it first dynamically selects feasible adversarial vehicles and collision types, and then progressively projects action sequences using contact point, relative heading, and severity constraints. It improves the collision rate to 46.4%/83.1% on nuScenes/nuPlan in an 80-frame closed-loop simulation, while achieving a collision type matching rate of 84.3%/85.6%.

CooperScene: Multi-Modal Cooperative Autonomy Benchmark with C-V2X Communication Characterization

CooperScene is the first collaborative autonomous driving dataset that synchronously captures C-V2X communication characteristics (throughput, latency, packet loss rate, jitter) and multi-modal sensor data (LiDAR, camera, GNSS/IMU) in real-world road scenarios. It covers interactions among three intelligent vehicles and one road-side unit (RSU) across diverse scenes such as intersections, highway ramps, and parking lots. Benchmark evaluations on this dataset reveal that the mAP of existing cooperative perception methods drops by up to 49% under realistic C-V2X bandwidth constraints compared to ideal network assumptions, highlighting the massive gap between idealized evaluations and deployable performance.

Deconfounded Lifelong Learning for Autonomous Driving via Dynamic Knowledge Spaces

DeLL introduces lifelong learning to closed-loop end-to-end autonomous driving for the first time. It utilizes a Dirichlet Process Mixture Model (DPMM) to construct dual dynamic knowledge spaces (explicit and implicit) and uses dynamically growing knowledge anchors as mediator variables to achieve deconfounding via front-door adjustment, achieving superior lifelong learning performance over PackNet and ER on Bench2Drive with zero extra storage overhead.

DiverseAD: A Large-Scale Driving Dataset with Diverse Atmospheric Conditions

DiverseAD provides both a 150K-scene driving dataset covering diverse atmospheric conditions and a dual-anchor feature-learning method: trajectory direction patterns and relationships from frozen DINOv3 features guide visual representations, reducing average L2 trajectory error on DiverseAD from 1.44 m for fine-tuned FSDrive to 1.24 m.

Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout

Modeling "how the in-cabin driver will move in the future" as a rollout problem in a latent space, causally driven by out-of-cabin traffic events: utilizing frozen Qwen3-VL features as the perception interface, a dual-stream architecture separately encodes traffic and the driver. A "gated causal injection" mechanism allows the external environment to unidirectionally modulate the autoregressive evolution of the driver's internal state. This is ultimately decoded into the human skeleton trajectory for the next 5 steps (augmented by behavioral/emotional semantics as auxiliary regularization), achieving robust long-term prediction on safety-critical, high-motion sequences in the AIDE dataset.

DriveVA: Video Action Models are Zero-Shot Drivers

DriveVA proposes a unified video-action world model that jointly decodes future video prediction and trajectory planning within a single DiT diffusion generation process. Leveraging the spatio-temporal priors of a large-scale video generation model (Wan2.2-TI2V-5B), it achieves strong zero-shot cross-domain generalization, obtaining 90.9 PDMS on NAVSIM, and reducing the average L2 error and collision rate by 78.9%/52.5% and 83.3%/52.4% on nuScenes and Bench2Drive, respectively.

DriveWeaver: Point-Conditioned Video Inpainting for Controllable Vehicle Insertion in Autonomous Driving Simulation

DriveWeaver proposes a video diffusion inpainting framework using vehicle point cloud renderings as pixel-level conditions. By employing a lightweight PointAdapter to inject geometric conditions into a frozen Wan2.1 diffusion transformer backbone, it generates foreground vehicles with consistent lighting integration and precise geometry. Additionally, a global-local hierarchical inpainting strategy is designed to eliminate long-horizon drift, and the generation results can be further distilled into 3D Gaussian Splatting for real-time rendering.

ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving

A unified understanding-generation framework, ExploreVLA, is proposed. It introduces future RGB + depth image generation as a dense world modeling objective within the VLA driving model. The image prediction uncertainty of the world model is leveraged as an intrinsic exploration reward. Under a safety-gated GRPO, the policy is enabled to surpass the imitation learning distribution and discover diverse yet safe driving behaviors, achieving state-of-the-art results of \(93.7\) PDMS and \(88.8\) EPDMS on NAVSIM.

FLM-Occ: Feed-forward Likelihood Maximization for Efficient Indoor Occupancy Prediction

FLM-Occ redefines indoor monocular occupancy prediction from voxel classification to voxel distribution estimation. By training the network with Maximum Likelihood Estimation (MLE) to predict the mixture model parameters of the scene in a feed-forward manner, it outperforms the previous state-of-the-art method (which requires 1200 Gaussian primitives) with only 32 superquadric primitives, while achieving a \(3.7\times\) speedup in inference.

FrozenDrive: Zero-Shot Text-Guided Driving Scene Generation and Data Augmentation

FrozenDrive completely freezes the pretrained Stable Diffusion backbone with zero added parameters, achieving both multi-view and temporal consistency purely by rearranging the inputs of self-attention (concatenating multi-view features and injecting reference frames). This preserves the original diffusion prior to enable zero-shot text-guided driving scene generation. Directed synthesis of rare adverse weather data (e.g., night, rain, snow) via text prompts significantly enhances downstream perception and planning robustness.

Geometry-Preserving in 3D Gaussian Splatting for LiDAR-Camera Extrinsic Calibration

This work proposes GeoP-Calib, which mitigates the "geometric decay" issue in 3DGS calibration through two designs: Dense Depth Anchoring (DDA, which aggregates multi-view LiDAR point clouds to establish a dense depth prior) and Gradient Decoupling (GD, which blocks the backpropagation of photometric loss to Gaussian spatial parameters). It achieves translation errors significantly superior to existing targetless calibration methods on KITTI-360 and KITTI.

Hybrid Event-Frame Sensors: Unified Noise Modeling, Calibration, and Simulation

Proposes the first unified statistical noise model and calibration pipeline for hybrid event-frame sensors, and constructs H-ESIM, a simulator capable of generating RAW frames and event streams with noise distributions matching real sensors, validating its sim-to-real transfer capabilities on video frame interpolation and deblurring tasks.

HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-training

HilDA distills LiDAR backbones using the "hierarchical semantic structure + CLS global context" from Vision Foundation Models (VFMs) rather than just the final layer features. It additionally incorporates a conditional diffusion task predicting future BEV occupancy to complement spatio-temporal geometric information, setting a new SOTA across multiple downstream tasks in cross-modal distillation: segmentation, detection, scene flow, and semantic occupancy.

Horizon3D: Sparse Radar-Camera Fusion for Long-Range 3D Perception in Autonomous Driving

Horizon3D proposes a sparse radar-camera fusion framework for long-range autonomous driving perception. It utilizes Gaussian primitives to capture object-level details and sparse BEV features to encode scene-level context. Additionally, it addresses the dilemma of long-range sparsity and high-speed large displacements simultaneously through dual-path temporal fusion (BEV path for multi-frame accumulation + Gaussian path for object-by-object motion propagation). On TruckScenes, it outperforms the previous state-of-the-art radar-camera fusion method by +3.0 NDS / +1.6 mAP, while achieving faster inference speed than existing BEV fusion methods.

HSDF-Lane: Height-Aligned Signed Distance Field with Semantic Lane Prior for 3D Lane Detection

HSDF-Lane replaces traditional sparse slope anchors with a vertically-aligned signed distance field (HSDF) to implicitly model road surfaces. Through differentiable rendering, it simultaneously outputs accurate height maps and surface-aligned features, while introducing Lane-aware Semantic Positional Encoding (LSPE) to inject lane existence priors into Transformer queries. This approach achieves dual SOTA in both 3D lane detection and height estimation on the OpenLane benchmark.

LaGen: Towards Autoregressive LiDAR Scene Generation

LaGen introduces the autoregressive framework to LiDAR scene generation for the first time. Starting from a single-frame point cloud and conditioning on 3D bounding boxes and ego-vehicle states, it generates high-fidelity, long-sequence LiDAR scenes frame-by-frame, supporting interactive closed-loop simulation.

LeAD-M3D: Leveraging Asymmetric Distillation for Real-Time Monocular 3D Detection

LeAD-M3D integrates three core components on top of an image-only YOLOv10-M3D baseline: Asymmetric MixUp Denoising Distillation (A2D2), a 3D-aware Consistent Matcher (CM3D) that incorporates 3D overlap into matching scores, and Confidence Gated Inference (CGI3D) that runs the 3D regression head only on high-confidence regions. Without relying on LiDAR, stereo images, or geometric priors, it achieves state-of-the-art accuracy on KITTI, Waymo, and Rope3D, while running up to 3.6 times faster than models of comparable accuracy and exceeding 60 FPS under TensorRT for its largest variant.

Long-term Traffic Simulation via Structured Autoregressive Modeling

RosettaSim models long-term multi-agent traffic simulation as a structured autoregressive sequence generation problem. By utilizing frozen/partially frozen LLMs (Qwen2.5-0.5B) as structural priors, it projects scene topology, agent states, and generation intents into variable-length token sequences to uniformly process motion prediction and agent population dynamics. Meanwhile, it proposes a Retrieval-based Traffic Evaluation (RTE) framework, using semantically similar real-world scenes as reference anchors instead of global distribution matching, achieving SOTA on both short-term and long-term simulation on WOSAC.

OmniNWM: Omniscient Driving Navigation World Models

OmniNWM integrates the three elements of driving world models—state, action, and reward—into a unified probabilistic framework. It jointly generates pixel-aligned panoramic RGB, semantics, depth, and 3D occupancy. By decoupling motion control from camera rig geometry through normalized panoramic Plücker ray maps, it achieves zero-shot cross-camera transfer. Furthermore, it directly derives dense rewards from the generated occupancy volume, enabling closed-loop evaluation of planning agents within the generated world.

Open-Vocabulary BEV Segmentation with 3D-Aware Geometric Constraints

This work proposes the first open-vocabulary BEV segmentation framework, OVBEVSeg. By reversing the geometric reasoning path (establishing 2D-BEV correspondence with unsupervised 3D detection \(\rightarrow\) optimizing geometry via BEV-aware 3DGS \(\rightarrow\) distilling to a feed-forward network), it lifts VLM semantics from 2D to the BEV space, outperforming closed-set methods by 15.3 mIoU on novel classes in nuScenes with faster inference speed.

PLOT: Pseudo-Labeling via Object Tracking for Monocular 3D Object Detection

PLOT proposes a 3D pseudo-labeling framework that requires no auxiliary sensors or model retraining. By establishing cross-frame point-level correspondences via dense point tracking, decoupling camera ego-motion and object motion estimation from background/object trajectories, and introducing a global object memory to maintain long-term identity consistency, the framework aligns and fuses multi-view partial observations into complete object pseudo-point clouds. It achieves performance comparable to fully supervised methods on benchmarks like KITTI, KITTI-360, and Waymo, and generalizes well to non-driving scenarios.

PriorEye: Geospatial Visual Priors for End-to-End Autonomous Driving

PriorEye anchors offline-collected street-view images to planned routes to construct "geospatial visual priors." These priors are injected into end-to-end driving policies via a dual-memory architecture, equipping the agent with human-driver-like prospective perception while maintaining robustness under both sensor failures and prior corruption.

Real-Time Source-Free Object Detection

This paper constructs RT-SFOD, a real-time source-free domain adaptive object detection framework based on YOLOv10 (the first NMS-free dual-head detector). It proposes Dual-Head Pseudo-Label Fusion (DHF) to recover objects missed by the O2O head while maintaining high precision, alongside a Multi-Scale Adaptive Representation Diversification Loss (MARD) to combat feature rank degradation caused by domain shift. On four domain shift benchmarks, it achieves a 1.4-3.5% mAP improvement with 1.3x faster inference speed and ~2x parameter compression.

RESOLVE: A Multi-Resolution and Multi-Modal Dataset for Roadside Cooperative Perception

RESOLVE is the first real-world multi-resolution, multi-modal roadside cooperative perception dataset. By simultaneously deploying three LiDARs with 16/64/128 channels at real urban intersections while strictly maintaining sensor configuration and environmental consistency, it systematically evaluates the impact of LiDAR resolution variation on 3D object detection, multi-object tracking, and cooperative perception. It reveals how multi-modal fusion compensates for perception degradation under sparse point clouds and exposes the domain drift issue caused by the mismatch between training and inference resolutions.

Rethinking Training & Inference for Forecasting: Linking Winner-Take-All back to GMMs

This paper reveals that the widely used Winner-Take-All (WTA) loss in autonomous driving trajectory prediction is fundamentally a K-Means clustering objective rather than a Gaussian Mixture Model (GMM) likelihood. It is precisely this mismatch of "GMM modeling + WTA training" that leads to uninformative mode probabilities and over-segmentation. On this basis, the authors propose two training-free post-processing methods—test-time weighted merging and one-step EM fine-tuning—which significantly improve mode sorting quality and displacement accuracy.

StreamOcc: Streaming Dense Voxel Representations for 3D Occupancy Prediction

StreamOcc integrates the streaming update paradigm with dense 3D voxel representations for occupancy prediction for the first time. It addresses the challenges of warping distortion and dynamic object feature degradation through rectified streaming voxel aggregation (StreamAgg) and query-guided aggregation (QueryAgg), achieving state-of-the-art (SOTA) performance on SurroundOcc and Occ3D-nuScenes with a real-time latency of 83.3ms/frame and a memory footprint of 2.8GB.

Towards Metric-Agnostic Trajectory Forecasting

This paper advocates that trajectory forecasting should only learn a "well-calibrated predictive distribution," treating conflicting metrics like minFDE and soft mAP as downstream tasks. By utilizing a set of retraining-free sampling policies (TraDiE policies) during evaluation, the optimal \(K\) trajectories for each metric are extracted from the same distribution, allowing a single DONUT-NLL model to simultaneously achieve SOTA on both distance-based and window-based metrics on Waymo.

Understanding Cross-Rig Generalization in Automotive Perception: a Multi-Rig Benchmark and Rig Variation Metrics

This paper constructs PCCR, a simulation benchmark containing 14 systematically designed camera rigs, and proposes two geometric descriptors based on calibration parameters: Rig Variance (RigV) and Rig Contrastive Distance (RigCD). Experiments demonstrate that camera translation discrepancy is the dominant factor in cross-rig generalization performance degradation, and RigCD can predict the ranking of transfer difficulty between different rigs, achieving a Spearman rank correlation of up to 0.80.

UniDrive-WM: Unified Understanding, Planning and Generation World Model for Autonomous Driving

UniDrive-WM jointly achieves scene understanding, trajectory planning, and trajectory-conditioned future image generation within a single VLM framework. Through a differentiable bidirectional coupling of planning and generation, it improves the Driving Score from 77.74 to 79.31 in Bench2Drive closed-loop evaluation, reduces the L2 trajectory error by 7.3%, and decreases the collision rate by 10.4%. Furthermore, it systematically analyzes the speed-fidelity trade-off between two complementary image generation branches (discrete autoregressive and continuous diffusion).

UniTeD: Unified Temporal Diffusion for Joint Perception and Planning in Autonomous Driving

UniTeD integrates the anchor queries of perception (detection, mapping, motion prediction) and planning into a unified diffusion denoising process for joint modeling. It utilizes a Temporal Transition Module (TTM) to align the noise levels of historical and current frames, and employs an Anchor Refresh Strategy (ARS) to alleviate the training-inference distribution shift in sparse diffusion. Using only cameras, it outperforms discriminative and planning-only diffusion methods on NAVSIM and Bench2Drive, achieving 90.2 PDMS / 87.3 DS.

Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings

This paper proposes the TransferAtlas framework, which employs a Graph Neural Network encoder to map 24 trajectory prediction datasets jointly into a 32-dimensional unified latent space. In this space, each dataset is represented as a Gaussian distribution. The KL divergence between two distributions quantifies the difficulty of transferring from the source domain to the target domain. This achieves a Spearman rank correlation coefficient of 0.811 for zero-shot transfer, offering principled guidance for dataset selection, pre-training source recommendation, and foundation model construction.

WarpI2I: Image Warping for Image-to-Image Translation

This paper proposes a model-agnostic, parameter-free warp-unwarp framework that spatially enlarges salient regions before encoding and subsequently reverses the warping, preserving fine-grained structural information in LDM image translation without increasing the latent space resolution. Meanwhile, a lightweight FLUX-based synthetic data pipeline is proposed, yielding significant improvements with minimal overhead for human/driving scene relighting and weather/time-of-day driving translation tasks.