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Silhouette Based Generic Model Adaptation for Marker-Less Motion Capturing

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Human Motion – Understanding, Modeling, Capture and Animation (HuMo 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4814))

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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.

We gratefully acknowledge funding by the Max-Planck Center for Visual Computing and Communication.

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Authors and Affiliations

  1. Max-Planck-Institut für Informatik, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany

    Martin Sunkel, Bodo Rosenhahn & Hans-Peter Seidel

Authors
  1. Martin Sunkel
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  2. Bodo Rosenhahn
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  3. Hans-Peter Seidel
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Ahmed Elgammal Bodo Rosenhahn Reinhard Klette

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© 2007 Springer-Verlag Berlin Heidelberg

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Sunkel, M., Rosenhahn, B., Seidel, HP. (2007). Silhouette Based Generic Model Adaptation for Marker-Less Motion Capturing. In: Elgammal, A., Rosenhahn, B., Klette, R. (eds) Human Motion – Understanding, Modeling, Capture and Animation. HuMo 2007. Lecture Notes in Computer Science, vol 4814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75703-0_9

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  • DOI: https://doi.org/10.1007/978-3-540-75703-0_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75702-3

  • Online ISBN: 978-3-540-75703-0

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