VEPHand: View-Efficient Photometric Hand Performance Capture at Scale
Robust, high-fidelity 3D hand capture, while fundamental to digital human creation, remains challenging with practical multi-view systems that balance rich photometry with the geometric ambiguities of reconstruction arising from limited viewpoint density. This paper presents an end-to-end pipeline for dynamic hand performance capture and registration, specifically designed for view-efficient setups ($\sim$20 views). We address key challenges with two primary innovations. First, to overcome reconstruction difficulties like limited view overlap and background clutter, our mask-free neural method robustly extracts detailed hand geometry and appearance from unmasked images using scene paramet...
| Authors | Zhengyang Shen, Kai-Hung Chang, Erroll Wood et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 June 2026 |
| Last updated at source | 18 June 2026 |
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