  In this paper, we propose a structured feature learning framework to reason
the correlations among body joints at the feature level in human pose
estimation. Different from existing approaches of modelling structures on score
maps or predicted labels, feature maps preserve substantially richer
descriptions of body joints. The relationships between feature maps of joints
are captured with the introduced geometrical transform kernels, which can be
easily implemented with a convolution layer. Features and their relationships
are jointly learned in an end-to-end learning system. A bi-directional tree
structured model is proposed, so that the feature channels at a body joint can
well receive information from other joints. The proposed framework improves
feature learning substantially. With very simple post processing, it reaches
the best mean PCP on the LSP and FLIC datasets. Compared with the baseline of
learning features at each joint separately with ConvNet, the mean PCP has been
improved by 18% on FLIC. The code is released to the public.
