A deep structure for human pose estimation.

Articulated human pose estimation in unconstrained conditions is a great challenge. We propose a deep structure that represents a human body in different granularity from coarse-to-fine for better detecting parts and describing spatial constrains between different parts. Typical approaches for this problem just utilize a single level structure, which is difficult to capture various body appearances and hard to model high-order part dependencies. In this paper, we build a three layer Markov network to model the body structure that separates the whole body to poselets (combined parts) then to parts representing joints. Parts at different levels are connected through a parent–child relationship to represent high-order spatial relationships. Unlike other multi-layer models, our approach explores more reasonable granularity for part detection and sophisticatedly designs part connections to model body configurations more effectively. Moreover, each part in our model contains different types so as to capture a wide range of pose modes. And our model is a tree structure, which can be trained jointly and favors exact inference. Extensive experimental results on two challenging datasets show the performance of our model improving or being on-par with state-of-the-art approaches. [ABSTRACT FROM AUTHOR]