DeVI: Physics-based Dexterous Human-Object Interaction via Synthetic Video Imitation
Physics-based character control for dexterous Human-Object Interaction (HOI) relies heavily on motion-capture data, which limits its applicability beyond captured scenarios. Meanwhile, video generative models can synthesize realistic HOI videos for unseen objects and scenarios directly from text prompts, but 3D interactions learned from such synthetic data remain kinematic without ensuring physical plausibility. Imitating these videos with physics-based control is a promising direction, yet unreliable object tracking and hand-object misalignment make it difficult to obtain accurate imitation targets. We present DeVI (Dexterous Video Imitation), a novel framework that learns physics-based ...
| Authors | Hyeonwoo Kim, Jeonghwan Kim, Kyungwon Cho et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 April 2026 |
| Last updated at source | 8 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.