Proposal maps driven MCMC for estimating human body pose in static images

This paper addresses the problem of estimating human body pose in static images. This problem is challenging due to the high dimensional state space of body poses, the presence of pose ambiguity, and the need to segment the human body in an image. We use an image generative approach by modeling the human kinematics, the shape and the clothing probabilistically. These models are used for deriving a good likelihood measure to evaluate samples in the solution, space. We adopt a data-driven MCMC framework for searching the solution space efficiently. Our observation data include the face, head-shoulders contour, skin color blobs, and ridges; and they provide evidences on the positions of the head, shoulders and limbs. To translate these inferences into pose hypotheses, we introduce the use of 'proposal maps', which is an efficient way of consolidating the evidence and generating 3D pose candidates during the MCMC search. As experimental results show, the proposed technique estimates the human 3D pose accurately on various test images.