2D Action Recognition Serves 3D Human Pose Estimation
3D human pose estimation in multi-view settings benefits from embeddings of human actions in low-dimensional manifolds, but the complexity of the embeddings increases with the number of actions. Creating separate, action-specific manifolds seems to be a more practical solution. Using multiple manifolds for pose estimation, however, requires a joint optimization over the set of manifolds and the human pose embedded in the manifolds. In order to solve this problem, we propose a particle-based optimization algorithm that can efficiently estimate human pose even in challenging in-house scenarios. In addition, the algorithm can directly integrate the results of a 2D action recognition system as prior distribution for optimization. In our experiments, we demonstrate that the optimization handles an 84D search space and provides already competitive results on HumanEva with as few as 25 particles.
(a) Silhouettes are extracted by background subtraction. (b) Tracks are built over the entire sequence and classified by a 2D action recognition system. (c) Confidences of each action are used to distribute the particles over the action-specific manifolds. (d) Final pose is obtained by optimizing over the manifolds.
Video ~40MB (AVI)
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Gall J., Yao A., and van Gool L., Optimizing Over a Set of Manifolds (PDF), Technical Report 271, Computer Vision Laboratory, ETH Zurich, Switzerland, June 2010.