Neural Human Pose Prior

2025-07-16

AI Summary

This work addresses the challenge of modeling probabilistic distributions of human poses represented as 6D rotations—i.e., elements on the SO(3) manifold. We propose a normalizing flow-based neural prior that explicitly respects the geometric structure of SO(3). Methodologically, we introduce the first integration of the RealNVP architecture with invertible Gram–Schmidt orthogonalization, enabling manifold-aware density estimation for 6D rotation representations. Our novel inverse Gram–Schmidt procedure ensures strict satisfaction of rotational constraints while preserving bijectivity and tractable likelihood evaluation. The resulting model exhibits strong representational capacity, training stability, and framework-agnostic compatibility. Experiments demonstrate substantial improvements in pose prior accuracy and cross-scenario generalization on motion capture and reconstruction tasks. Our approach establishes a new paradigm for probabilistic human motion modeling grounded in differential geometry and deep generative learning.

Technology Category

Machine Learning: Learning with ManifoldsReasoning under Uncertainty: Relational Probabilistic ModelsComputer Vision: Biometrics, Face, Gesture & Pose

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsUser Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendationResponsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Modeling neural prior over human body poses
• Learning flexible density over 6D rotation poses
• Integrating pose priors into motion capture pipelines

Innovation

• Uses normalizing flows for pose modeling
• Leverages RealNVP for flexible density learning
• Inverts Gram-Schmidt for stable rotation learning