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Jie Wang, Guang Li, Zhijian Huang, Chenxu Dang, Hangjun Ye, Yahong Han, Long Chen

The significance of cross-view 3D geometric modeling capabilities for autonomous driving is self-evident, yet existing Vision-Language Models (VLMs) inherently lack this capability, resulting in their mediocre performance. While some promising approaches attempt to mitigate this by constructing Q&A data for auxiliary training, they still fail to fundamentally equip VLMs with the ability to comprehensively handle diverse evaluation protocols. We thus chart a new course, advocating for the infusion of VLMs with the cross-view geometric grounding of mature 3D foundation models, closing this critical capability gap in autonomous driving. In this spirit, we propose a novel architecture, VGGDrive, which empowers Vision-language models with cross-view Geometric Grounding for autonomous Driving. Concretely, to bridge the cross-view 3D geometric features from the frozen visual 3D model with the VLM's 2D visual features, we introduce a plug-and-play Cross-View 3D Geometric Enabler (CVGE). The CVGE decouples the base VLM architecture and effectively empowers the VLM with 3D features through a hierarchical adaptive injection mechanism. Extensive experiments show that VGGDrive enhances base VLM performance across five autonomous driving benchmarks, including tasks like cross-view risk perception, motion prediction, and trajectory planning. It's our belief that mature 3D foundation models can empower autonomous driving tasks through effective integration, and we hope our initial exploration demonstrates the potential of this paradigm to the autonomous driving community.

Hakan Emre Gedik, Shashank Gupta, Alan Bovik

No-reference image quality assessment (NR IQA) has recently benefited from deep and multimodal models, yet many SOTA systems still violate at least one basic requirement: they either discard critical quality cues via aggressive resizing, fail to generalize across resolutions, cannot be jointly trained on heterogeneous IQA datasets with mismatched MOS scales, or require prohibitive computation. We present ReLIQS, a model for Resolution-agnostic Learning for Image Quality with Saliency, which is resolution-agnostic, preserves original-resolution quality cues, learns from multiple subjective studies, and remains computationally efficient and budget-adaptive. ReLIQS is a CLIP-based multiscale patch-driven architecture that learns both where to look and how to judge quality. Fixed-size patches are sampled across multiple resolutions, including the original resolution, and encoded with a CLIP vision backbone. A lightweight Perceptual Importance Estimator then predicts IQA-specific importance maps to select a small set of informative patches, and a Quality Aspect Module aggregates their embeddings into a single image-level score. Across authentic, synthetic, and AIGC benchmarks spanning diverse resolutions and distortions, ReLIQS generalizes better than strong CNN-, CLIP-, and MLLM-based baselines with matching or reduced computational cost.

Yuanhang Lei, Tao Cheng, Xingxuan Li, Boming Zhao, Siyuan Huang, Ruizhen Hu, Peter Yichen Chen, Hujun Bao, Zhaopeng Cui

Achieving real-time physics-based animation that generalizes across diverse 3D shapes and discretizations remains a fundamental challenge. We introduce PhysSkin, a physics-informed framework that addresses this challenge. In the spirit of Linear Blend Skinning, we learn continuous skinning fields as basis functions lifting motion subspace coordinates to full-space deformation, with subspace defined by handle transformations. To generate mesh-free, discretization-agnostic, and physically consistent skinning fields that generalize well across diverse 3D shapes, PhysSkin employs a new neural skinning fields autoencoder which consists of a transformer-based encoder and a cross-attention decoder.Furthermore, we also develop a novel physics-informed self-supervised learning strategy that incorporates on-the-fly skinning-field normalization and conflict-aware gradient correction, enabling effective balancing of energy minimization, spatial smoothness, and orthogonality constraints.PhysSkin shows outstanding performance on generalizable neural skinning and enables real-time physics-based animation.

Sixiao Zheng, Minghao Yin, Wenbo Hu, Xiaoyu Li, Ying Shan, Yanwei Fu

Video world models aim to simulate dynamic, real-world environments, yet existing methods struggle to provide unified and precise control over camera and multi-object motion, as videos inherently capture dynamics in the projected 2D image plane. To bridge this gap, we introduce VerseCrafter, a geometry-driven video world model that generates dynamic, realistic videos from a unified 4D geometric world state. Our approach is centered on a novel 4D Geometric Control representation, which encodes the world state as a static background point cloud and per-object 3D Gaussian trajectories. This representation captures each object's motion path and probabilistic 3D occupancy over time, providing a flexible, category-agnostic alternative to rigid bounding boxes and parametric models. We render 4D Geometric Control into 4D control maps for a pretrained video diffusion model, enabling high-fidelity, view-consistent video generation that faithfully follows the specified dynamics. To enable training at scale, we develop an automatic data engine and construct VerseControl4D, a real-world dataset of 35K training samples with automatically derived prompts and rendered 4D control maps. Extensive experiments show that VerseCrafter achieves superior visual quality and more accurate control over camera and multi-object motion than prior methods.

Hongyi Pan, Gorkem Durak, Halil Ertugrul Aktas, Andrea M. Bejar, Baver Tutun, Emre Uysal, Ezgi Bulbul, Mehmet Fatih Dogan, Berrin Erok, Berna Akkus Yildirim 等

Publicly available full-field digital mammography (FFDM) datasets remain limited in size, clinical annotations, and vendor diversity, hindering the development of robust models. We introduce LUMINA, a curated, multi-vendor FFDM dataset that explicitly encodes acquisition energy and vendor metadata to capture clinically relevant appearance variations often overlooked in existing benchmarks. This dataset contains 1824 images from 468 patients (960 benign, 864 malignant), with pathology-confirmed labels, BI-RADS assessments, and breast-density annotations. LUMINA spans six acquisition systems and includes both high- and low-energy imaging styles, enabling systematic analysis of vendor- and energy-induced domain shifts. To address these variations, we propose a foreground-only pixel-space alignment method ("energy harmonization") that maps images to a low-energy reference while preserving lesion morphology. We benchmark CNN and transformer models on three clinically relevant tasks: diagnosis (benign vs. malignant), BI-RADS classification, and density estimation. Two-view models consistently outperform single-view models. EfficientNet-B0 achieves an AUC of 93.54% for diagnosis, while Swin-T achieves the best macro-AUC of 89.43% for density prediction. Harmonization improves performance across architectures and produces more localized Grad-CAM responses. Overall, LUMINA provides (1) a vendor-diverse benchmark and (2) a model-agnostic harmonization framework for reliable and deployable mammography AI.

Pei Liu, Qingtian Ning, Xinyan Lu, Haipeng Liu, Weiliang Ma, Dangen She, Xianpeng Lang, Jun Ma

The pursuit of autonomous agents with predictive cognitive world models is hindered by a fundamental flaw in current vision-language models (VLMs): they lack cognitive inertia. Operating on isolated snapshots, these models cannot form a temporally coherent world view, leading to erratic decision jitter and a failure to execute complex, multi-step maneuvers. To remedy this, we introduce CogDriver, a framework designed to build a coherent world model by instilling this crucial cognitive property. Our work makes two key contributions: (1) We present CogDriver-Data, a large-scale vision-language-action dataset whose narrative annotations provide the supervisory signal for learning the temporal dynamics of a world model. (2) We develop the CogDriver-Agent, an architecture featuring a sparse temporalmemory to maintain a stable internal state, the foundation of a world model. This is enabled by a spatiotemporal knowledge distillation approach that explicitly teaches decision coherence. Comprehensive experiments validate our paradigm: CogDriver-Agent achieves a 22% increase in the closed-loop Driving Score on Bench2Drive and a 21% reduction in mean L2 error on nuScenes, establishing a new state-of-the-art. These significant gains in both long-term decision-making and imitation accuracy provide strong evidence that our agent is developing a more stable internal world model.

Feng Yang, Jie Zhao, Fulin Luo, Anyong Qin, Tiecheng Song, Yue Zhao, Chenqiang Gao, Junwei Han

Current few-shot action recognition methods achieve impressive performance by learning representative prototypes and designing diverse video matching strategies. However, these approaches typically face two critical limitations: i) prototypes learned through implicit sample interactions lack clear semantic correspondence between query-support pairs, limiting their class representativeness; ii) the independent design of prototype learning and matching mechanisms creates a potential incompatibility between prototype representations and matching strategies. To address these limitations, we propose a Match-guided Prototype Learning (MPL) method comprising two key components: enhanced match (E-Match) and key-frame extraction match (K-Match). E-Match explicitly enhances prototype learning in class-specific embeddings by incorporating the matched semantics of query samples, while K-Match further refines the prototype representation through key-frame matching at the fine-grained frame level. Additionally, we propose a Cross-Shot Attention Aggregator (CSA-Aggregator) that dynamically aggregates adjacent frames across support samples, thereby obtaining a prototype representation that captures intra-class shared action patterns. In this way, the proposed MPL effectively mines coarse-to-fine, match-guided semantic information from query-support pairs to generate discriminative class prototypes, and improve the compatibility of prototype representation with the match mechanism. Extensive evaluations on four public datasets confirm that MPL achieves superior performance over leading few-shot action recognition techniques. The source code is available at https://github.com/jayzh-research/MPL-FSAR.

Linchun Wu, Qin Zou, Yuanhao Yue, Zhongyuan Wang

Point cloud anomaly detection is crucial in automated manufacturing, with reconstruction-based diffusion methods emerging as a mainstream solution. However, these approaches still face two major challenges: (1) geometry violation, where random noise perturbations deviate from local surface normals, causing structural distortion; and (2) undistinguished reference regions, where uniformly applied coarse anomaly embeddings during denoising blur normal details and impede accurate anomaly recovery. To address these issues, we propose AARD, a geometry-aligned and anomaly-aware diffusion reconstruction framework. We argue that high-fidelity anomaly detection requires a principled reformulation of the diffusion process: noise should align with geometry to preserve structures, and reconstruction can be better guided by anomaly-aware references to discriminatively recover normal details while correcting defects. AARD progressively aligns noise directions with vertex normals while maintaining vertex-graph consistency, and employs an adaptive transformer that assigns normal references to anomalous regions and input references to normal areas. Experiments on Anomaly-ShapeNet and Real3D-AD show that AARD consistently outperforms state-of-the-art approaches, achieving superior geometric fidelity and robust anomaly localization.

Yueming Pan, Ruoyu Feng, Qi Dai, Yuqi Wang, Wenfeng Lin, Mingyu Guo, Chong Luo, Nanning Zheng

Latent Diffusion Models (LDMs) inherently follow a coarse-to-fine generation process, where high-level semantic structure is generated slightly earlier than fine-grained texture. This indicates the preceding semantics potentially benefit the texture generation by providing a semantic anchor. Recent advances have integrated semantic priors from pretrained visual encoders to further enhance LDMs, yet they still denoise semantic and VAE-encoded texture synchronously, neglecting such ordering. Observing these, we propose Semantic-First Diffusion (SFD), a latent diffusion paradigm that explicitly prioritizes semantic formation. SFD first constructs composite latents by combining the compact semantic latent, which is extracted from pretrained visual encoder via a dedicated Semantic VAE, with the texture latent. The core of SFD is to denoise the semantic and texture latents asynchronously using separate noise schedules: semantics precede textures by a temporal offset, providing clearer high-level guidance for texture refinement and enabling natural coarse-to-fine generation. On ImageNet 256x256 with guidance, SFD achieves FID 1.06 (LightningDiT-XL) and FID 1.04 (1.0B LightningDiT-XXL), while achieving up to 100xfaster convergence than original DiT without guidance. SFD also improves existing methods like ReDi and VA-VAE, demonstrating the effectiveness of asynchronous, semantics-led modeling.

Yogesh Kulkarni, Pooyan Fazli

Multimodal reasoning over long-horizon video is challenging due to the need for precise spatiotemporal fusion and alignment across modalities. While recent methods such as Group Relative Policy Optimization (GRPO) have shown promise in this domain, they suffer from three key limitations: (1) data inefficiency from their on-policy design, (2) a vanishing advantage problem, where identical or near-identical rewards within a group eliminate the learning signal by producing zero-valued advantages, and (3) uniform credit assignment that fails to emphasize critical reasoning steps. We introduce AVATAR (Audio-Video Agent for Alignment and Reasoning, a framework that addresses these limitations through two core components: (1) an off-policy training architecture that improves sample efficiency and resolves vanishing advantages by reusing past experiences with greater reward diversity, and (2) Temporal Advantage Shaping (TAS), a credit assignment strategy that emphasizes early (planning) and late (synthesis) reasoning phases. AVATAR achieves strong performance across various benchmarks, outperforming the Qwen2.5-Omni baseline by +5.4 on MMVU, +4.9 on OmniBench, and +4.5 on Video-Holmes. Furthermore, it surpasses standard GRPO by +3.7 on OmniBench and +1.9 on Video-Holmes, while demonstrating 5 times sample efficiency, requiring 80 fewer generated completions to reach target performance.

Ruofan Liang, Norman Müller, Ethan Weber, Duncan Zauss, Nandita Vijaykumar, Peter Kontschieder, Christian Richardt

We present a novel approach for interactive light editing in indoor scenes from a single multi-view scene capture. Our method leverages a generative image-based light decomposition model that factorizes complex indoor scene illumination into its constituent light sources. This factorization enables independent manipulation of individual light sources, specifically allowing control over their state (on/off), chromaticity, and intensity. We further introduce multi-view lighting harmonization to ensure consistent propagation of the lighting decomposition across all scene views. This is integrated into a relightable 3D Gaussian splatting representation, providing real-time interactive control over the individual light sources. Our results demonstrate highly photorealistic lighting decomposition and relighting outcomes across diverse indoor scenes. We evaluate our method on both synthetic and real-world datasets and provide a quantitative and qualitative comparison to state-of-the-art techniques.

Yukai Ma, Honglin He, Selina Song, Wayne Wu, Bolei Zhou

Long-horizon navigation in complex urban environments relies heavily on continuous human operation, which leads to fatigue, reduced efficiency, and safety concerns. Shared autonomy, where a Vision-Language AI agent and a human operator collaborate on maneuvering the mobile machine, presents a promising solution to address these issues. However, existing shared autonomy methods often require humans and AI to operate within the same action space, leading to high cognitive overhead. We present Assistive Urban Robot Autonomy (AURA), a new multi-modal framework that decomposes urban navigation into high-level human instruction and low-level AI control. AURA incorporates a Spatial-Aware Instruction Encoder to align various human instructions with visual and spatial context. To facilitate training, we construct MM-CoS, a large-scale dataset comprising teleoperation and vision-language descriptions. Experiments in simulation and the real world demonstrate that AURA effectively follows human instructions, reduces manual operation effort, and improves navigation stability, while enabling online adaptation. Moreover, under similar takeover conditions, our shared autonomy framework reduces the frequency of takeovers by more than 44%. Demo video and more detail are provided in the project page.

Xin Yu, Xiaojuan Qi, Zhengqi Li, Kai Zhang, Richard Zhang, Zhe Lin, Eli Shechtman, Tianyu Wang, Yotam Nitzan

We introduce the Self-Evaluating Model (Self-E), a novel, from-scratch training approach for text-to-image generation that supports any-step inference. Self-E learns from data similarly to a Flow Matching model, while simultaneously employing a novel self-evaluation mechanism: it evaluates its own generated samples using its current score estimates, effectively serving as a dynamic self-teacher. Unlike traditional diffusion or flow models, it does not rely solely on local supervision, which typically necessitates many inference steps. Unlike distillation-based approaches, it does not require a pretrained teacher. This combination of instantaneous local learning and self-driven global matching bridges the gap between the two paradigms, enabling the training of a high-quality text-to-image model from scratch that excels even at very low step counts. Extensive experiments on large-scale text-to-image benchmarks show that Self-E not only excels in few-step generation, but is also competitive with state-of-the-art Flow Matching models at 50 steps. We further find that its performance improves monotonically as inference steps increase, enabling both ultra-fast few-step generation and high-quality long-trajectory sampling within a single unified model. To our knowledge, Self-E is the first from-scratch, any-step text-to-image model, offering a unified framework for efficient and scalable generation.

Zhuolin He, Jing Li, Guanghao Li, Xiaolei Chen, Jiacheng Tang, Siyang Zhang, Zhounan Jin, Feipeng Cai, Bin Li, Jian Pu 等

Dynamic scene reconstruction in autonomous driving remains a fundamental challenge due to significant temporal variations, moving objects, and complex scene dynamics. Existing feed-forward 3D models have demonstrated strong performance in static reconstruction but still struggle to capture dynamic motion. To address these limitations, we propose DynamicVGGT, a unified feed-forward framework that extends VGGT from static 3D perception to dynamic 4D reconstruction. Our goal is to model point motion within feed-forward 3D models in a dynamic and temporally coherent manner. To this end, we jointly predict the current and future point maps within a shared reference coordinate system, allowing the model to implicitly learn dynamic point representations through temporal correspondence. To efficiently capture temporal dependencies, we introduce a Motion-aware Temporal Attention (MTA) module that learns motion continuity. Furthermore, we design a Dynamic 3D Gaussian Splatting Head (DGSH) that explicitly models point motion by predicting Gaussian velocities using learnable motion tokens under scene flow supervision. It refines dynamic geometry through continuous 3D Gaussian optimization. Extensive experiments on autonomous driving datasets demonstrate that DynamicVGGT significantly outperforms existing methods in reconstruction accuracy and temporal consistency, achieving robust feed-forward 4D dynamic scene reconstruction under complex driving scenarios.

Jiandong Jin, Chenglong Li, Hao Feng, Andong Lu, Lili Huang, Jin Tang

Unaligned RGBT tracking aims to achieve robust target localization across spatially misaligned RGB and thermal infrared (TIR) videos, a crucial challenge for deploying RGBT tracking in real-world scenarios. Existing methods often calculate all cross-modal alignment parameters (i.e., spatial shift and scale change) simultaneously, but suffer from two major limitations. 1) They are difficult to adapt to different degrees of unaligned difficulty during tracking. 2) They usually require complex models to handle challenging scenarios, resulting in a large computational burden. To overcome these limitations, we propose a novel Progressive Multi-cue Alignment framework called PMATrack, which disentangles the calculation of cross-modal alignment parameters in a progressive manner and dynamically selects appropriate cues to handle different challenges, thereby enabling robust and efficient unaligned RGBT tracking. In particular, PMATrack divides the cross-modal alignment parameter estimation into three stages to progressively perform center offset computation, scale transformation estimation, and global refinement. At each stage, we design a difficulty-aware router to adaptively select the appropriate alignment expert based on the cross-modal alignment complexity, thereby reducing computational redundancy. In addition, we build a high-quality video benchmark called MUART244 to facilitate the comprehensive evaluation of different unaligned RGBT tracking algorithms. Extensive experiments demonstrate the outstanding performance of PMATrack against existing state-of-the-art methods. The code and dataset will be available at https://github.com/NOP1224/Unaligned_RGBT_Tracking.

Yuru Wang, Yue Zhou

Facial Action Unit (AU) detection suffers from limited annotated data, severe class imbalance, label noise, and confounding biases, which often lead to overfitting and degraded performance. We propose an Uncertainty-Driven Causal Transformer (UDCT) framework for robust AU detection by jointly modeling uncertainty and causal intervention. Specifically, we parameterize Transformer attention weights as Gaussian distributions to capture robust AU dependencies while explicitly modeling uncertainty in attention. We further introduce an uncertainty-aware loss reweighting strategy to alleviate the effects of class imbalance and label noise during training. In addition, we incorporate a causal intervention module to suppress confounder-dependent AU associations and encourage the model to focus on more stable and less biased AU relationships. Experiments on BP4D and DISFA demonstrate that UDCT achieves competitive performance with stronger robustness under noisy, imbalanced, and distribution-shifted settings.

Zhuojie Wu, Shijie Wang, Xin Yu

Video Large Language Models (VLLMs) encounter significant computational challenges due to the large volume of visual tokens generated from multiple frames. Existing visual token pruning methods fail to account for the uneven spatiotemporal information density, thus squandering scarce token budgets on regions with low information density. In this paper, we propose a training-free Metadata-guided Token Merging framework (MeToM) that leverages intrinsic video metadata to adaptively allocate budgets and merge visual tokens based on content complexity. Specifically, MeToM exploits residual data from codec metadata as spatial information density cues. It merges less informative regions during tokenization, avoiding redundant encoding and improving the efficiency of the visual encoder. Additionally, MeToM captures temporal variations in information density by utilizing the average Group of Pictures (GoP) packet size to represent scene complexity. This mechanism enables dynamic per-frame token allocation across time, assigning more tokens to content-complex frames and fewer tokens to information-sparse ones. Finally, we merge low-contribution visual tokens via multi-layer attention to reduce prefill FLOPs and the visual KV-cache footprint inside the LLM. Extensive experimental results demonstrate that MeToM outperforms prior state-of-the-art methods. Notably, it achieves a 2.65xinference speedup over the baseline VLLM without sacrificing accuracy.

Stefan Andreas Baumann, Jannik Wiese, Tommaso Martorella, Mahdi M. Kalayeh, Björn Ommer

Accurately anticipating how complex, diverse scenes will evolve requires models that represent uncertainty, simulate along extended interaction chains, and efficiently explore many plausible futures. Yet most existing approaches rely on dense video or latent-space prediction, expending substantial capacity on dense appearance rather than on the underlying sparse trajectories of points in the scene. This makes large-scale exploration of future hypotheses costly and limits performance when long-horizon, multi-modal motion is essential. We address this by formulating the prediction of open-set future scene dynamics as step-wise inference over sparse point trajectories. Our autoregressive diffusion model advances these trajectories through short, locally predictable transitions, explicitly modeling the growth of uncertainty over time. This dynamics-centric representation enables fast rollout of thousands of diverse futures from a single image, optionally guided by initial constraints on motion, while maintaining physical plausibility and long-range coherence. We further introduce OWM, a benchmark for open-set motion prediction based on diverse in-the-wild videos, to evaluate accuracy and variability of predicted trajectory distributions under real-world uncertainty. Our method matches or surpasses dense simulators in predictive accuracy while achieving orders-of-magnitude higher sampling speed, making open-set future prediction both scalable and practical.

Yuxin Qin, Ke Cao, Haowei Liu, Ao Ma, Fengheng Li, Honghe Zhu, Zheng Zhang, Run Ling, Wei Feng, Xuanhua He 等

E-commerce product poster generation aims to automatically synthesize a single image that effectively conveys product information by presenting a subject, text, and a designed style. Recent diffusion models with fine-grained and efficient controllability have advanced product poster synthesis, yet they typically rely on multi-stage pipelines, and simultaneous control over subject, text, and style remains underexplored. Such naive multi-stage pipelines also show three issues: poor subject fidelity, inaccurate text, and inconsistent style. To address these issues, we propose InnoAds-Composer, a single-stage framework that enables efficient tri-conditional control tokens over subject, glyph, and style. To alleviate the quadratic overhead introduced by naive tri-conditional token concatenation, we perform importance analysis over layers and timesteps and route each condition only to the most responsive positions, thereby shortening the active token sequence. Besides, to improve the accuracy of Chinese text rendering, we design a Text Feature Enhancement Module (TFEM) that integrates features from both glyph images and glyph crops. To support training and evaluation, we also construct a high-quality e-commerce product poster dataset and benchmark, which is the first dataset that jointly contains subject, text, and style conditions. Extensive experiments demonstrate that InnoAds-Composer significantly outperforms existing product poster methods without obviously increasing inference latency.

Ian Noronha, Heather Neave, Upinder Kaur

Understanding animal behavior requires modeling how bodies, objects, and other agents interact over time, not simply detecting isolated actions or estimating pose frame by frame. Existing animal video datasets target pose estimation or coarse, passively observed actions, and rarely provide the structured, multi-entity interaction annotations needed to study behavioral dynamics. We introduce MooCap, a multi-view video benchmark for animal-object-human interaction understanding under controlled experimental protocols. MooCap contains 42 hours of synchronized multi-camera video from 43 individually tested cows across seven standardized interaction scenarios, including novel environment, novel object, novel human, human approach, unfamiliar conspecifics (restricted and unrestricted) and Dam reunion (restricted and unrestricted). Recordings are densely annotated with 23 fine-grained behaviors, 39 body keypoints across 157 test sessions, 4 spatial zones, and 43 subjects, describing interactions among subjects, objects, humans, and other cattle. We establish three benchmarks on MooCap: (1) dense temporal action segmentation over 1200-1500-second sequences; (2) pose-based behavior and interaction recognition from keypoint trajectories; and (3) longitudinal behavioral classification linking adult behaviors with rearing conditions. Benchmarking results reveal that state-of-the-art temporal segmentation models achieve only 66.4% frame accuracy and 30.6% F1@0.5, with performance degrading further during interaction-heavy segments. Overall, MooCap bridges multi-view pose estimation, multi-entity tracking, and structured behavioral protocols to enable interaction-aware models for animal behavior analysis.