6 papers
Population-Scalable Multi-Agent World Modeling
Renjie Zhao, Yuxiang Wu, Mingyu Zhang +9
World models have recently achieved impressive progress in visual prediction and interactive generation, but extending them to multi-agent environments introduces a fundamental sca…
HAT-4D: Lifting Monocular Video for 4D Multi-Object Interactions via Human-Agent Collaboration
Jiaxin Li, Yuxiang Wu, Zhenkai Zhang +11
Extracting dynamic 4D object interactions from massive, in-the-wild monocular videos offers a highly efficient data collection pathway for scaling Embodied AI and training VLAs. Ho…
Tac-DINO: Learning Vision-Tactile Features with Patch Alignment
Hong Li, Yankang Dong, Yue Xu +8
Touch is the primary medium through which humans interact with the environment. Currently, tactile learning mainly focuses on image-level pretraining or alignment. However, tactile…
Beyond Static Vision: Scene Dynamic Field Unlocks Intuitive Physics Understanding in Multi-modal Large Language Models
Nanxi Li, Xiang Wang, Yuanjie Chen +3
While Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in image and video understanding, their ability to comprehend the physical world has become…
Efficient and Scalable Monocular Human-Object Interaction Motion Reconstruction
Boran Wen, Ye Lu, Sirui Wang +7
Generalized robots must learn from diverse, large-scale human-object interactions (HOI) to operate robustly in the real world. Monocular internet videos offer a nearly limitless an…
Reconstructing In-the-Wild Open-Vocabulary Human-Object Interactions
Boran Wen, Dingbang Huang, Zichen Zhang +6
Reconstructing human-object interactions (HOI) from single images is fundamental in computer vision. Existing methods are primarily trained and tested on indoor scenes due to the l…