MRM: Multi-View 3D Human Pose Estimation Based on Regression with Multivariate Joint Distribution

Abstract

In the field of multi-view three-dimensional (3D) human pose estimation, there are primarily two approaches: heatmap-based and regression-based models. Regression-based models require less computational effort than heatmap-based models but are less accurate. This study proposes a regression-based model called multi-view 3D human pose estimation based on regression with multivariate joint distribution (MRM), which achieves accuracy comparable to heatmap-based models while using lower computational resources in multi-view 3D human pose estimation. Specifically, this model employs a flow-based method to learn the multivariate joint distribution of human pose data, enabling the regression-based model to capture nonlinear dependencies across different perspectives. Experimental results on two public datasets validate the accuracy and efficiency of the proposed model. Compared with heatmap-based methods, MRM reduces multiply-add operations by 32.3% while maintaining comparable prediction accuracy.

Keywords

human pose estimation / log-likelihood estimation / convolutional network / multivariate joint distribution