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Optimizing Over a Set of Manifolds

J. Gall, A. Yao and L. Van Gool
271, 2010
Computer Vision Lab, ETH Zuerich

Abstract

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. We give a comprehensive description of the algorithm proposed in ``2D Action Recognition Serves 3D Human Pose Estimation''.


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@Techreport{eth_biwi_00735,
  author = {J. Gall and A. Yao and L. Van Gool},
  title = {Optimizing Over a Set of Manifolds},
  year = {2010},
  month = {June},
  number = {271},
  institution = {Computer Vision Lab, ETH Zuerich},
  keywords = {optimization, human pose estimation, dimensionality reduction, action recognition}
}