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] .
Sun et al. REF propose a structure-aware loss function that operates on bone based pose parameterization.
Compositional Human Pose Regression
2017 IEEE International Conference on Computer Vision (ICCV)
2017 IEEE International Conference on Computer Vision (ICCV)
Computer Science