Humoto: A 4D Dataset of Mocap Human Object Interactions
Jiaxin Lu, Chun-Hao Paul Huang, Uttaran Bhattacharya, Qixing Huang, Yi Zhou
摘要
We present Human Motions with Objects (HUMOTO), a high-fidelity dataset of human-object interactions for motion generation, computer vision, and robotics applications. Featuring 735 sequences (7,875 seconds at 30 fps), HUMOTO captures interactions with 63 precisely modeled objects and 72 articulated parts. Our innovations include a scene-driven LLM scripting pipeline creating complete, purposeful tasks with natural progression, and a mocap-and-camera recording setup to effectively handle occlusions. Spanning diverse activities from cooking to outdoor picnics, HUMOTO preserves both physical accuracy and logical task flow. Professional artists rigorously clean and verify each sequence, minimizing foot sliding and object penetrations. We also provide benchmarks compared to other datasets. HUMOTO's comprehensive full-body motion and simultaneous multi-object interactions address key data-capturing challenges and provide opportunities to advance realistic human-object interaction modeling across research domains with practical applications in animation, robotics, and embodied AI systems. Project: https://jiaxin-lu.github.io/humoto/ .
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引用它的顶会 Paper5
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- RoMo: A Large-Scale, Richly Organized Dataset and Semantic Taxonomy for Human Motion GenerationJiahao Zhang, Joseph Liu, Young-Yoon Lee, Seonghyeon Moon 等CVPR 2026 · 被引用 2 次
- RF-HOI: Recognize Human-Object Interaction with Radio Frequency SignalsLihao Wang, Linlu Gao, Jiacan Yu, Yanyu Lin 等UbiComp 2026
- Recovering Physically Plausible Human-Object Interactions from Monocular VideosDingbang Huang, Etienne Vouga, Qixing Huang, Georgios PavlakosCVPR 2026
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- InterDiff: Generating 3D Human-Object Interactions with Physics-Informed DiffusionSirui Xu, Zhengyuan Li, Yu-Xiong Wang, Liang-Yan GuiICCV 2023 · 被引用 201 次
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