Probabilistic Deformable Surface Tracking from Multiple Videos

  • Cedric Cagniart
  • Edmond Boyer
  • Slobodan Ilic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)


In this paper, we address the problem of tracking the temporal evolution of arbitrary shapes observed in multi-camera setups. This is motivated by the ever growing number of applications that require consistent shape information along temporal sequences. The approach we propose considers a temporal sequence of independently reconstructed surfaces and iteratively deforms a reference mesh to fit these observations. To effectively cope with outlying and missing geometry, we introduce a novel probabilistic mesh deformation framework. Using generic local rigidity priors and accounting for the uncertainty in the data acquisition process, this framework effectively handles missing data, relatively large reconstruction artefacts and multiple objects. Extensive experiments demonstrate the effectiveness and robustness of the method on various 4D datasets.


Visual Hull Reprojection Error Multiple Video Data Acquisition Process 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.


  1. 1.
    Vlasic, D., Baran, I., Matusik, W., Popović, J.: Articulated mesh animation from multi-view silhouettes. In: ACM SIGGRAPH 2008 (2008)Google Scholar
  2. 2.
    Cagniart, C., Boyer, E., Ilic, S.: Free-from mesh tracking: a patch-based approach. In: IEEE CVPR (2010)Google Scholar
  3. 3.
    Mundermann, L., Corazza, S., Andriacchi, T.P.: Accurately measuring human movement using articulated icp with soft-joint constraints and a repository of articulated models. In: CVPR (2007)Google Scholar
  4. 4.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, series B (1977)Google Scholar
  5. 5.
    Gall, J., Stoll, C., de Aguiar, E., Theobalt, C., Rosenhahn, B., Seidel, H.P.: Motion capture using joint skeleton tracking and surface estimation. In: IEEE CVPR 2009 (2009)Google Scholar
  6. 6.
    Gall, J., Rosenhahn, B., Brox, T., Seidel, H.P.: Optimization and filtering for human motion capture. IJCV 87 (2010)Google Scholar
  7. 7.
    Corazza, S., Mündermann, L., Gambaretto, E., Ferrigno, G., Andriacchi, T.P.: Markerless motion capture through visual hull, articulated icp and subject specific model generation. IJCV 87 (2010)Google Scholar
  8. 8.
    Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE PAMI 14 (1992)Google Scholar
  9. 9.
    de Aguiar, E., Stoll, C., Theobalt, C., Ahmed, N., Seidel, H.P., Thrun, S.: Performance capture from sparse multi-view video. In: ACM SIGGRAPH 2008 (2008)Google Scholar
  10. 10.
    Horaud, R.P., Niskanen, M., Dewaele, G., Boyer, E.: Human motion tracking by registering an articulated surface to 3-d points and normals. IEEE PAMI 31 (2009)Google Scholar
  11. 11.
    Horaud, R.P., Forbes, F., Yguel, M., Dewaele, G., Zhang, J.: Rigid and articulated point registration with expectation conditional maximization. IEEE PAMI (2010)Google Scholar
  12. 12.
    Myronenko, A., Song, X.: Point-set registration: Coherent point drift. IEEE PAMI (2010)Google Scholar
  13. 13.
    Starck, J., Hilton, A.: Surface capture for performance based animation. In: IEEE Computer Graphics and Applications 27(3) (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Cedric Cagniart
    • 1
  • Edmond Boyer
    • 2
  • Slobodan Ilic
    • 1
  1. 1.Technische Universität München 
  2. 2.Grenoble Universités - INRIA Rhône-Alpes 

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