Human Pose Estimation Using Deep Structure Guided Learning

Baole Ai, Yu Zhou, Yao Yu, Sidan Du

Abstract: In this paper, we propose a novel approach to incorporate structure knowledge into Convolutional Neural Networks (CNNs) for articulated human pose estimation from a single still image. Recent research on pose estimation adopt CNNs as base blocks to combine with other graphical models. Different from existing methods using features from CNNs to model the tree structure, we directly use the structure pose prior to guide the learning of CNN. First, we introduce a deep CNN with effective receptive fields which capture the holistic context of the whole image. Second, limb loss is used as intermediate supervision of CNN to learn the correlations of joints. Both parts and joints features are extracted in the middle of neural network and then are used to guide the following network learning. The proposed framework can exploit an implicit structure model of human body. Only using one stage and without any complex post processing, our method achieves state-of-art results on both FLIC and LSP benchmarks.