AI RESEARCH
PAD-Hand: Physics-Aware Diffusion for Hand Motion Recovery
arXiv CS.CV
•
ArXi:2603.26068v1 Announce Type: new Significant advancements made in reconstructing hands from images have delivered accurate single-frame estimates, yet they often lack physics consistency and provide no notion of how confidently the motion satisfies physics. In this paper, we propose a novel physics-aware conditional diffusion framework that refines noisy pose sequences into physically plausible hand motion while estimating the physics variance in motion estimates. Building on a MeshCNN-Transformer backbone, we formulate Euler-Lagrange dynamics for articulated hands.