  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 and is competitive
with state-of-the-art results on MPII.
