Compositional Human Pose Regression

Citations Over TimeTop 1% of 2017 papers

Abstract

Regression based methods are not performing as well as detection based methods for human pose estimation. A central problem is that the structural information in the pose is not well exploited in the previous regression methods. In this work, we propose a structure-aware regression approach. It adopts a reparameterized pose representation using bones instead of joints. It exploits the joint connection structure to define a compositional loss function that encodes the long range interactions in the pose. It is simple, effective, and general for both 2D and 3D pose estimation in a unified setting. Comprehensive evaluation validates the effectiveness of our approach. It significantly advances the state-of-the-art on Human3.6M [20] and is competitive with state-of-the-art results on MPII [3].

Related Papers

• → Learning to Refine Human Pose Estimation(2018)112 cited
• → Domain-Translated 3D Object Pose Estimation(2020)23 cited
• → Fast 6D object pose estimation of shell parts for robotic assembly(2021)7 cited
• → An Object Pose Estimation System Using A Single Camera(2005)12 cited
• → Fast 6D Object Pose Estimation of Shell Parts for Robotic Assembly(2021)1 cited