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arXiv · 2605.18102

DanceHMR: Hand-Aware Whole-Body Human Mesh Recovery from Monocular Videos

Wenhao Shen·Ming Zhou·Hengyuan Zhang·Siyuan Bian·Youjiang Xu·Yuan Zhang
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Abstract

Monocular video human mesh recovery is essential for digital humans, avatar animation, and embodied simulation, where both temporal stability and expressive whole-body motion are required. Existing video HMR methods produce coherent body motion but often overlook detailed hand articulation, while image-based whole-body methods recover SMPL-X meshes independently per frame, often leading to jittery and inaccurate hand motion. We present a temporally coherent whole-body HMR framework for challenging in-the-wild monocular videos. Our model unifies body context and part-specific hand observations through residual body-hand fusion, enabling stable body motion and detailed hand recovery within a single temporal architecture. We further introduce close-up-aware augmentation to improve robustness under upper-body framing. Experiments on whole-body and body-only benchmarks demonstrate improved hand reconstruction and competitive body accuracy. Our method also produces temporally stable and 2D-consistent SMPL-X motion in challenging real-world videos.

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Wenhao Shen, Ming Zhou, Hengyuan Zhang, Siyuan Bian, Youjiang Xu, Yuan Zhang. 2026-06-03. DanceHMR: Hand-Aware Whole-Body Human Mesh Recovery from Monocular Videos. https://arxiv.org/abs/2605.18102

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