V2P-Manip: Learning Dexterous Manipulation from Monocular Human Videos
Achieving autonomous robotic dexterous manipulation requires precise, human-like action sequences at scale. As a scalable supplement to costly teleoperation data, extracting trajectories with both visual fidelity and physical plausibility from monocular videos represents a promising frontier in embodied AI. To this end, we introduce V2P-Manip, an efficient framework designed to learn dexterous manipulation policies directly from human demonstration videos. We establish an efficient, integrated pipeline encompassing 3D asset acquisition, trajectory estimation, and dexterous policy learning. To bridge the gap between visual perception and physical constraints, we introduce a two-stage refin...
| Authors | Kaihan Chen, Yanming Shao, Haifeng Ji et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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