Mixture of Parts Revisited: Expressive Part Interactions for Pose Estimation

TL;DR

This paper enhances the Mixture of Parts model for human pose estimation by effectively handling self-occlusion and symmetric part confusion, leading to significant performance improvements while maintaining computational efficiency.

Contribution

It introduces methods to address self-occlusion and symmetric limb confusion within a tree-structured model, improving accuracy without high computational costs.

Findings

Improved pose estimation accuracy on standard datasets.

Achieved results comparable to state-of-the-art methods.

Maintained faster inference times than complex models.

Abstract

Part-based models with restrictive tree-structured interactions for the Human Pose Estimation problem, leaves many part interactions unhandled. Two of the most common and strong manifestations of such unhandled interactions are self-occlusion among the parts and the confusion in the localization of the non-adjacent symmetric parts. By handling the self-occlusion in a data efficient manner, we improve the performance of the basic Mixture of Parts model by a large margin, especially on uncommon poses. Through addressing the confusion in the symmetric limb localization using a combination of two complementing trees, we improve the performance on all the parts by atmost doubling the running time. Finally, we show that the combination of the two solutions improves the results. We report results that are equivalent to the state-of-the-art on two standard datasets. Because of maintaining the tree-structured interactions and only part-level modeling of the base Mixture of Parts model, this is achieved in time that is much less than the best performing part-based model.