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Silhouette Based Generic Model Adaptation for Marker-Less Motion Capturing

  • Martin Sunkel
  • Bodo Rosenhahn
  • Hans-Peter Seidel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4814)

Abstract

This work presents a marker-less motion capture system that incorporates an approach to smoothly adapt a generic model mesh to the individual shape of a tracked person. This is done relying on extracted silhouettes only. Thus, during the capture process the 3D model of a tracked person is learned.

Depending on a sparse number of 2D-3D correspondences, that are computed along normal directions from image sequences of different cameras, a Laplacian mesh editing tool generates the final adapted model. With the increasing number of frames an approach for temporal coherence reduces the effects of insufficient correspondence data to a minimum and guarantees smooth adaptation results. Further, we present experiments on non-optimal data that show the robustness of our algorithm.

Keywords

Motion Capture Kinematic Chain Human Body Model Walking Sequence Reference Mesh 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Martin Sunkel
    • 1
  • Bodo Rosenhahn
    • 1
  • Hans-Peter Seidel
    • 1
  1. 1.Max-Planck-Institut für Informatik, Stuhlsatzenhausweg 85, 66123 SaarbrückenGermany

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