Learning human-object manipulation presents significant challenges due to its fine-grained and contact-rich nature of the motions involved. Traditional physics-based animation requires extensive modeling and manual setup, and more importantly, it neither generalizes well across diverse object morphologies nor scales effectively to real-world environment. To address these limitations, we introduce LOME, an egocentric world model that can generate realistic human-object interactions as videos conditioned on an input image, a text prompt, and per-frame human actions, including both body poses and hand gestures. LOME injects strong and precise action guidance into object manipulation by jointly estimating spatial human actions and the environment contexts during training. After finetuning a pretrained video generative model on videos of diverse egocentric human-object interactions, LOME demonstrates not only high action-following accuracy and strong generalization to unseen scenarios, but also realistic physical consequences of hand-object interactions, e.g., liquid flowing from a bottle into a mug after executing a ``pouring'' action. Extensive experiments demonstrate that our video-based framework significantly outperforms state-of-the-art image based and video-based action-conditioned methods and Image/Text-to-Video (I/T2V) generative model in terms of both temporal consistency and motion control. LOME paves the way for photorealistic AR/VR experiences and scalable robotic training, without being limited to simulated environments or relying on explicit 3D/4D modeling.
Causal-Entity Reflected Egocentric Traffic Accident Video Synthesis
Xi’an Jiaotong University（西安交通大学）；National University of Singapore（国立新加坡大学）；Nanyang Technological University（南洋理工大学）；Cleveland State University（克利夫兰州立大学）
EgoMotion: Hierarchical Reasoning and Diffusion for Egocentric Vision-Language Motion Generation
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS)（智能信息处理重点实验室，计算技术研究所（ICT），中国科学院（CAS））；Jilin University (JLU)（吉林大学（JLU））；Beijing University of Posts and Telecommunications (BUPT)（北京邮电大学（BUPT））；University of the Chinese Academy of Sciences（中国科学院大学）
Diff-IP2D: Diffusion-Based Hand-Object Interaction Prediction on Egocentric Videos
IRMV Lab, the Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China（IRMV实验室，自动化系，上海交通大学，上海200240，中国）；College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China（智能科学与技术学院，国防科技大学，长沙410073，中国）；Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China（电子工程系，上海交通大学，上海200240，中国）
Beyond Successful Robot Demonstrations: Learning from Human Interactions and Modeling Failure Dynamics
Zhejiang University（浙江大学）；Beijing Zhongguancun Academy（北京中关村学院）；University of Science and Technology of China（中国科学技术大学）；Eastern Institute of Technology（东方理工学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
StableHand: Quality-Aware Flow Matching for World-Space Dual-Hand Motion Estimation from Egocentric Video
Mohamed bin Zayed University of Artificial Intelligence（莫扎德·本·泽德人工智能大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Imperial College London（伦敦帝国理工学院）
COMBO: Compositional World Models for Embodied Multi-Agent Cooperation
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；IIIS, Tsinghua University（清华大学信息科学技术学院）；Peking University（北京大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Honda Research Institute USA（美国本田研究所）；MIT（麻省理工学院）
Intention-Conditioned Long-Term Human Egocentric Action Forecasting
Technische Universität Wien (TU Wien)（维也纳技术大学）；Artificial Intelligence Graduate School (AIGS), Ulsan National Institute of Science and Technology (UNIST)（蔚山科学技术院人工智能研究生院）；Institute of Robotics and Mechatronics, German Aerospace Center（德国航空航天中心机器人与机电一体化研究所）
How You Move Tells What You'll Do: Trajectory-Conditioned Egocentric Prediction
Khoury College of Computer Sciences, Northeastern University, Boston（北德文斯克学院，东北大学，波士顿）；Korea Advanced Institute of Science and Technology, Daejeon（韩国科学技术院，大田）；Department of Mathematics and Computer Science, University of Catania, Italy（卡塔尼亚大学数学与计算机科学系，意大利）
MacJEPA: Missingness-Robust Audio-Visual Recognition from Untrimmed Egocentric Videos
Peter L. Reichertz Institute for Medical Informatics, Hannover Medical School（汉诺威医学院彼得·L·赖歇茨医学信息学研究所）；Lower Saxony Center for Artificial Intelligence and Causal Methods in Medicine (CAIMed)（下萨克森州医学人工智能与因果方法中心（CAIMed））
Current 6D object pose methods consist of deep CNN models fully optimized for a single object but with its architecture standardized among objects with different shapes. In contrast to previous works, we explicitly exploit each object's distinct topological information i.e. 3D dense meshes in the pose estimation model, with an automated process and prior to any post-processing refinement stage. In order to achieve this, we propose a learning framework in which a Graph Convolutional Neural Network reconstructs a pose conditioned 3D mesh of the object. A robust estimation of the allocentric orie…
UC Berkeley（加州大学伯克利分校）；Facebook AI Research（Facebook人工智能研究院）；UIUC（伊利诺伊大学厄巴纳-香槟分校）
Planning at a higher level of abstraction instead of low level torques improves the sample efficiency in reinforcement learning, and computational efficiency in classical planning. We propose a method to learn such hierarchical abstractions, or subroutines from egocentric video data of experts performing tasks. We learn a self-supervised inverse model on small amounts of random interaction data to pseudo-label the expert egocentric videos with agent actions. Visuomotor subroutines are acquired from these pseudo-labeled videos by learning a latent intent-conditioned policy that predicts the inf…
World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Tsinghua University（清华大学）；Harbin Institute of Technology（哈尔滨工业大学）；Wuhan AI Research（武汉人工智能研究院）
Video summarization plays an important role in selecting keyframe for understanding a video. Traditionally, it aims to find the most representative and diverse contents (or frames) in a video for short summaries. Recently, query-conditioned video summarization has been introduced, which considers user queries to learn more user-oriented summaries and its preference. However, there are obstacles in text queries for user subjectivity and finding similarity between the user query and input frames. In this work, (i) Image is introduced as a query for user preference (ii) a mathematical model is pr…
