Neural Human Pose Prior

Michal Heker; Sefy Kagarlitsky; David Tolpin

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arXiv · 2507.12138

Neural Human Pose Prior

Michal Heker·Sefy Kagarlitsky·David Tolpin

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Abstract

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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BibTeX RIS

Michal Heker, Sefy Kagarlitsky, David Tolpin. 2025-07-16. Neural Human Pose Prior. https://arxiv.org/abs/2507.12138

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