用流模型学习人体姿态的先验分布，提升动作捕捉精度。
Neural Human Pose Prior
- 用RealNVP流模型建模6D旋转表示的姿态分布
- 通过逆Gram-Schmidt过程稳定训练并保持兼容性
- 适合需要精确姿态先验的动作重建与捕捉任务
我们提出一种基于归一化流的、数据驱动的人体姿态神经先验建模方法。与启发式或低表达能力的方法不同，该方法利用RealNVP在6D旋转格式下学习灵活的姿态分布。为解决6D旋转流形上的分布建模难题，训练中反向执行Gram-Schmidt过程，实现稳定学习并保持与基于旋转框架的下游方法兼容。所提架构和训练流程与具体框架无关，易于复现。通过定性和定量评估验证了该先验的有效性，并通过消融实验分析其影响。本工作为人体现象捕捉与重建流程中集成姿态先验提供了坚实的概率基础。
原文摘要 · Abstract (English)
We introduce a principled, data-driven approach for modeling a neural prior over human body poses using normalizing flows. Unlike heuristic or low-expressivity alternatives, our method leverages RealNVP to learn a flexible density over poses represented in the 6D rotation format. We address the challenge of modeling distributions on the manifold of valid 6D rotations by inverting the Gram-Schmidt process during training, enabling stable learning while preserving downstream compatibility with rotation-based frameworks. Our architecture and training pipeline are framework-agnostic and easily reproducible. We demonstrate the effectiveness of the learned prior through both qualitative and quantitative evaluations, and we analyze its impact via ablation studies. This work provides a sound probabilistic foundation for integrating pose priors into human motion capture and reconstruction pipelines.
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