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] .
Recently, a practical pose re-parameterization based on bones instead of joints has been proposed together with a compositional loss REF .
Compositional Human Pose Regression
2017 IEEE International Conference on Computer Vision (ICCV)
2017 IEEE International Conference on Computer Vision (ICCV)
Computer Science