We propose a method for human pose estimation which extends common unary and pairwise terms of graphical models with a global foreground term. Given knowledge of per pixel foreground, a pose should not only be plausible according to the graphical model but also explain the foreground well. However, while inference on a standard tree-structured graphical model for pose estimation can be computed easily and very efficiently using dynamic programming, this no longer holds when the global foreground term is added to the problem. We therefore propose a branch and bound based algorithm to retrieve the globally optimal solution to our pose estimation problem. To keep inference tractable and avoid the obvious combinatorial explosion, we propose upper bounds allowing for an intelligent exploration of the solution space. We evaluated our method on several publicly available datasets, showing the benefits of our method.
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