An Optimization Based Framework for Human Pose Estimation

In computer vision community, human pose estimation and nonrigid shape recovery have evolved into different sub-fields. The state-of-the-art optimization techniques have been applied to the problem of deformable surface reconstruction successfully and recent methods in this area have focused on designing formulations that are easier to solve. In general, these techniques lay their success on the assumption that sufficient 2-D-3-D correspondences can be detected. By contrast, confronted with the similar ambiguity problem, many techniques for human pose estimation adopt stochastic searching or discriminative predictions, which allow for more generative image cues. However, the global optimization cannot be guaranteed via the stochastic methods; and discriminative techniques usually suffer from inaccuracy. In this letter, we absorb ideas from both domains and propose a unified approach for articulated human pose estimation. Specifically, we optimize the human pose to account for the discriminative pose prediction, bone length preservation in parallel with the point-to-point image observation. Moreover, the L-2 norm minimization is solved iteratively as a linear system with high computational efficiency.