Multi-source deep learning for human pose estimation

Visual appearance score, appearance mixture type and deformation are three important information sources for human pose estimation. This paper proposes to build a multi-source deep model in order to extract non-linear representation from these different aspects of information sources. With the deep model, the global, high-order hu-man body articulation patterns in these information sources are extracted for pose estimation. The task for estimat-ing body locations and the task for human detection are jointly learned using a unified deep model. The proposed approach can be viewed as a post-processing of pose esti-mation results and can flexibly integrate with existing meth-ods by taking their information sources as input. By extract-ing the non-linear representation from multiple information sources, the deep model outperforms state-of-the-art by up to 8.6 percent on three public benchmark datasets. 1