Human Upper Body Pose Estimation in Static Images

Mun Wai Lee, Isaac Cohen

Abstract: Estimating human pose in static images is challenging due to the high dimensional state space, presence of image clutter and ambiguities of image observations. We present an MCMC framework for estimating 3D human upper body pose. A generative model, comprising of the human articulated structure, shape and clothing models, is used to formulate likelihood measures for evaluating solution candidates. We adopt a data-driven proposal mechanism for searching the solution space efficiently. We introduce the use of proposal maps, which is an efficient way of implementing inference proposals derived from multiple types of image cues. Qualitative and quantitative results show that the technique is effective in estimating 3D body pose over a variety of images.