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Video-Conditioned Generative Joint 2D-3D Hand Motion Recovery

Paper recorded by Signals 4 on 2026-10-07 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-07 on arXiv · recorded by Signals 4 on 2026-10-08

Category: cs.CV · 计算机视觉 · first seen 2026-10-08

Abstract

Recovering faithful 3D hand motion from video remains challenging due to frequent occlusions and incomplete visual observations, which make frame-wise pose estimates unreliable and temporally inconsistent. To address this problem, we propose JoHan, a unified generative framework that recovers hand motion directly from video sequences without relying on intermediate per-frame pose predictions. Trai

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#319 most recent of 756 cs.CV papers we have recorded · ↑ newer: GRACE: Generation-aware latent compression for efficient video generat · ↓ older: QuadTok: Quadtree Visual Tokenizer for Autoregressive Image Generation
Cite this page: Video-Conditioned Generative Joint 2D-3D Hand Motion Recovery: the #319 most recent of 756 cs.CV papers we have recorded (as of 2026-10-07). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/video-conditioned-generative-joint-2d-3d-hand-motion-recovery.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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