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版本更新 · v3arXiv:2606.15015

NEXUS: Neural Energy Fields for Physically Consistent Contact-Rich 3D Object Dynamics

Qizhen Ying, Guangming Wang, Yangchen Pan, Victor Adrian Prisacariu, Brian Sheil, Yixiong Jing

原文摘要

Physics-grounded video generation requires controllable 3D object dynamics that remain physically consistent under contact, deformation, and external forcing. Existing trajectory-based methods often model isolated physical effects, making it difficult to compose conservative and non-conservative dynamics in contact-rich 3D scenes. We present NEXUS, a neural energy-field framework for contact-rich 3D object dynamics. NEXUS represents each object as a structural graph and constructs dynamic object-object and object-environment contact graphs. Inspired by Hamiltonian Neural Networks, NEXUS formulates motion through scalar energy and dissipation terms rather than directly predicting states or accelerations. Conservative effects, including gravity and elastic deformation, are composed as additive energy terms, while non-conservative effects such as damping and impact-induced energy loss are modeled with learned Rayleigh-style dissipation. Forces are derived by differentiating the energy and dissipation functions and rolled out with a multi-substep semi-implicit integrator. Across controlled trajectory benchmarks, NEXUS improves long-horizon accuracy over representative learned and physics-structured dynamics baselines under varying mechanical properties and physical-effect compositions. We further show that NEXUS trajectories provide effective guidance for contact-rich video generation, improving physical plausibility while maintaining competitive visual quality.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.12297

EgoEV-HandPose: Egocentric 3D Hand Pose Estimation and Gesture Recognition with Stereo Event Cameras

Luming Wang, Hao Shi, Jiajun Zhai, Kailun Yang, Kaiwei Wang

原文摘要

Egocentric 3D hand pose estimation and gesture recognition are fundamental to understanding hand interactions from first-person observations. Robust perception remains challenging: frame-based cameras suffer from motion blur and limited dynamic range, while event-based approaches face ego-motion interference, monocular depth ambiguity, and limited real-world datasets. We propose EgoEV-HandPose, an end-to-end framework for joint 3D bimanual hand pose estimation and gesture recognition from stereo event streams. Its core module, KeypointBEV, combines sparse 3D joint hypotheses with geometry-aware stereo fusion and iterative reprojection-guided refinement to progressively resolve depth uncertainty and improve pose accuracy. We also introduce EgoEVHands, the first large-scale real-world stereo event dataset for egocentric 3D bimanual hand pose estimation and gesture recognition. Built upon EgoEvGesture recordings, it adds dense 2D/3D hand annotations and comprises 2,498 annotated stereo sequence pairs across 38 gesture classes. Extensive experiments demonstrate that EgoEV-HandPose achieves state-of-the-art performance on EgoEVHands, obtaining a mean per-joint position error (MPJPE) of 30.54mm and a gesture recognition accuracy of 86.87%, significantly outperforming RGB-based stereo and previous event-based methods. The framework exhibits exceptional robustness under low illumination and severe bimanual occlusion. Experiments on DHP19 further demonstrate the cross-dataset and cross-architecture generalizability of the KeypointBEV refinement design. Dataset and code will be released at https://github.com/ZJUWang01/EgoEV-HandPose.

cs.CV · cs.RO · eess.IV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2608.21290

VT-MUSE: Multimodal Unified Sequential Visuotactile Representation Learning for Manipulation

Congsheng Xu, Qiaochu Yang, Fangyuan Shi, Yifan Han, Baijun Chen, Yiming Wang, Haonan Zhao, Zhe Liu 等 12 位作者

原文摘要

We propose VT-MUSE, a Multimodal Unified SEquential representation learning framework for visuotactilemanipulation. Existing approaches often encode visual and tactile observations independently before fusion, limiting their ability to capture fine-grained cross-modal dependencies. Moreover, most methods focus on observations at the current time step and overlook the temporal evolution of contact. VT-MUSE addresses both limitations through a two-stage representation learning framework. In Stage I, modality specific encoders are jointly adapted via cross-modal temporal alignment and masked-view consistency. In Stage II, a conditional variational latent model processes masked visual sequences together with full tactile histories. Auxiliary decoders reconstruct the masked recent visual observations and predict tactile depth changes, encouraging the latent representation to retain both global visual context and local contact dynamics. The learned representation is subsequently integrated into a lightweight Transformer policy through gated cross-attention. On the simulation benchmark, VT-MUSE outperforms the strongest baseline evaluated on all tasks by 11 percentage points and also achieves substantial improvements in real-world experiments.

cs.RO · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.37918

Venus-DefakerOne2: A Vision-Forensics Foundation Model with Broader Coverage

Chenfan Qu, Yachao Liang, Mingqi Fang, Jiangwei Xie, Zhenming Wang, Baoyue Zhang, Changjiang Jiang, Zheng Chang 等 31 位作者

原文摘要

Real-world forgery is inherently heterogeneous: it manifests not only as digital manipulation but also as physical spoofing, and is carried not only by images but also by videos. Despite substantial progress, a unified anti-forgery foundation model covering such a broad threat spectrum remains absent. In this paper, we present Venus-DefakerOne2, which substantially extends the forensic capability boundary along both the forgery-form and media-type dimensions. Trained on tens of millions of open-source and in-house samples, it incorporates two key designs. Tide-Style-Tuning alternates LoRA and full-parameter fine-tuning to balance computational efficiency, generalization, and hard-sample fitting while mitigating catastrophic forgetting. Entropy-based Test Adaptation determines the optimal prediction modality by analyzing prediction entropy. Venus-DefakerOne2 achieves new state-of-the-art performance across a comprehensive suite of benchmarks, including DeepFake faces (96.7%), AI-generated images (93.3%), tampered documents (87.6%), manipulated natural images (89.6%), physical spoofs (93.1%), and AI-generated videos (96.0%). It significantly outperforms both specialized expert models and its predecessor, DefakerOne, establishing itself as a powerful and versatile foundation model for binary-decision forensic tasks.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2602.00122

VDE Bench: Evaluating The Capability of Image Editing Models to Modify Visual Documents

Hongzhu Yi, Yujia Yang, Yuanxiang Wang, Tong Li, Zhenyu Guan, Tianyu Zong, Jiahuan Chen, Chenxi Bao 等 21 位作者

原文摘要

Image editing models can modify visual content through natural language, but editing dense documents requires precise local changes amid tightly coupled text, tables, and layout. We introduce VDE Bench, an evaluation resource for studying instruction-guided editing of Chinese and English document images. VDE Bench contains 3,506 instruction-guided image editing samples, including 1,578 text-region edits and 1,928 table-region edits. The benchmark pairs editing instructions and reference images with annotated editing regions and structured OCR outputs. Its evaluation protocol combines region-level and full-page measurements to assess textual correctness, structural fidelity, and visual content preservation. By focusing on the interaction between local edits and their surrounding document context, VDE Bench complements existing visual text editing benchmarks and provides a resource for diagnosing document-specific editing failures.

cs.CV · cs.AI · cs.MM

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1422ACL Anthology · ACL 2026

Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training

Jihao Gu, Qihang Ai, Yingyao Wang, Pi Bu Alibaba Group, Jingxuan Xing Alibaba Group, Yue Cao, Zekun Zhu Alibaba Group, Wei Jiang 等 14 位作者

原文摘要

Vision-language model-based mobile agents have gained the ability to understand complex instructions and mobile screenshots, benefiting from reinforcement learning paradigms like Group Relative Policy Optimization (GRPO). However, existing approaches centers on offline training or local action-level rewards often trap agents in local optima, hindering effective exploration and error correction with the environment. Crucially, we find that directly applying task-level rewards often leads to convergence difficulties due to the sparse nature of GUI interactions. To address these challenges, we present Mobile-R1, a systematic training recipe that bridges atomic action execution and strategic task completion. We propose a hierarchical curriculum consisting of three stages: (1) format alignment for reasoning structure, (2) on-policy exploration with verifiable action feedback to ground basic execution, and (3) multi-turn task-level training with realistic environment to unlock exploration and self-correction. This hierarchical strategy effectively bootstraps the agent, significantly enhancing its capability for exploration and self-correction (the “Eureka” moments). Furthermore, addressing the critical scarcity of diverse GUI data in non-English ecosystems, we contribute a comprehensive Chinese mobile dataset covering 28 applications with 24,521 high-quality manual annotations, and establish a rigorous benchmark with 500 trajectories. We will open source all resources, including the dataset, benchmark, model weight, and codes: https://mobile-r1.github.io/Mobile-R1/.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.508ACL Anthology · ACL 2026

Towards Mitigating Modality Bias in Vision-Language Models for Temporal Action Localization

Jiaqi Li, Guangming Wang University of Cambridge, Shuntian Zheng The University of Warwick, Minzhe Ni, Xiaoman Lu, Guanghui Ye Hunan University, Yu Guan University of Warwick

原文摘要

Temporal Action Localization (TAL) requires identifying both the boundaries and categories of actions in untrimmed videos. While vision-language models (VLMs) offer rich semantics to complement visual evidence, existing approaches tend to overemphasize linguistic priors at the expense of visual performance, leading to a pronounced modality bias. We propose ActionVLM, a vision-language aggregation framework that systematically mitigates modality bias in TAL. Our key insight is to preserve vision as the dominant signal while adaptively exploiting language only when beneficial. To this end, we introduce (i) a debiasing reweighting module that estimates the language advantage—the incremental benefit of language over vision-only predictions—and dynamically reweights language modality accordingly, and (ii) a residual aggregation strategy that treats language as a complementary refinement rather than the primary driver. This combination alleviates modality bias, reduces overconfidence from linguistic priors, and strengthens temporal reasoning. Experiments on THUMOS14 show that our model outperforms state-of-the-art by up to 3.2% mAP. Our code is available at https://github.com/JiaqiLi404/ActionVLM

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表