Continuous Normalizing Flows for Uncertainty-Aware Human Pose Estimation

TL;DR

This paper introduces CFRE, a novel method integrating Continuous Normalizing Flows into regression models for human pose estimation, achieving improved accuracy and uncertainty quantification while maintaining computational efficiency.

Contribution

The paper proposes Continuous Flow Residual Estimation (CFRE), a new approach that enhances uncertainty modeling in human pose estimation by combining CNFs with regression models.

Findings

CFRE improves pose estimation accuracy.

CFRE provides better uncertainty quantification.

CFRE maintains computational efficiency.

Abstract

Human Pose Estimation (HPE) is increasingly important for applications like virtual reality and motion analysis, yet current methods struggle with balancing accuracy, computational efficiency, and reliable uncertainty quantification (UQ). Traditional regression-based methods assume fixed distributions, which might lead to poor UQ. Heatmap-based methods effectively model the output distribution using likelihood heatmaps, however, they demand significant resources. To address this, we propose Continuous Flow Residual Estimation (CFRE), an integration of Continuous Normalizing Flows (CNFs) into regression-based models, which allows for dynamic distribution adaptation. Through extensive experiments, we show that CFRE leads to better accuracy and uncertainty quantification with retained computational efficiency on both 2D and 3D human pose estimation tasks.