Monocular Pose Capture with a Depth Camera Using a Sums-of-Gaussians Body Model

  • Daniyar Kurmankhojayev
  • Nils Hasler
  • Christian Theobalt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8142)


We present a new markerless generative approach for Human Motion Tracking using a single depth camera. It is based on a Sums of Spatial Gaussians (SoGs) representation for modeling the scene. In contrast to existing systems our approach does not require a multi-view camera setup, exemplar database or training data. The proposed system is accurate, fast and capable of tracking complex motions including 360° turns and self-occlusion of limited duration. The motivation behind our approach is that representing the depth data and a given a priori human model by a SoGs, we can construct an efficient continuously differentiable similarity measure and estimate an optimal pose for each input frame using a local optimization algorithm (Modified Gradient Ascent Linear Search, MGALS).


Human Model Depth Image Body Model Depth Camera Camera Intrinsic Parameter 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Daniyar Kurmankhojayev
    • 1
  • Nils Hasler
    • 1
  • Christian Theobalt
    • 1
  1. 1.MPI InformatikGermany

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