让单目视频中的人体动作更自然，尤其手部细节更精准。
DanceHMR: Hand-Aware Whole-Body Human Mesh Recovery from Monocular Videos
- 用残差融合统一身体与手部信息，保持动作连贯性。
- 在真实场景下手部重建误差降低18%，身体精度媲美顶尖方法。
- 适合需要精细手部动作的虚拟人、动画和仿真应用。
单目视频人体网格恢复对数字人、角色动画和具身模拟至关重要，需兼顾时序稳定性和全身表达能力。现有视频级方法虽能生成连贯身体运动，但常忽略手部细节；图像级全身体方法独立处理每帧，导致手部抖动且不准确。本文提出一种适用于复杂真实场景单目视频的时序一致全身体态恢复框架。通过残差身体-手部融合机制，联合利用身体上下文与局部手部观测，在统一时序架构中实现稳定的身体运动与精细的手部重建。进一步引入近景感知增强策略，提升上半身构图下的鲁棒性。在全身体和仅身体基准测试上均验证了手部重建性能的提升，并达到与顶尖方法相当的体态精度。该方法在挑战性真实视频中可生成时序稳定且2D一致的SMPL-X动作序列。
原文摘要 · Abstract (English)
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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