Markerless Motion Capture of Complex Full-Body Movement for Character Animation

  • Andrew J. Davison
  • Jonathan Deutscher
  • Ian D. Reid
Part of the Eurographics book series (EUROGRAPH)


Vision-based full-body tracking aims to reproduce the performance of current commercial marker-based motion capture methods in a system which can be run using conventional cameras and without the use of special apparel or other equipment, improving usability in existing application domains and opening up new possibilities since the methods can be applied to image sequences acquired from any source. We present results from a system able to perform robust visual tracking with an articulated body model, using data from multiple cameras. Our approach to searching through the high-dimensional model configuration space is an algorithm called annealed particle filtering which finds the best fit to image data via multiple-layer propagation of a stochastic particle set. This algorithm efficiently searches the configuration space without the need for restrictive dynamical models, permitting tracking of agile, varied movement. The data acquired can readily be applied to the animation of CG characters. Movie files illustrating the results in this paper may be obtained from ~ ajd/HMC/.


Configuration Space Extend Kalman Filter Motion Capture Multiple Camera Foreground Segmentation 
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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  1. 1.
    A. Baumberg and D. Hogg. Generating spatiotemporal models from examples. In Proc. British Machine Vision Conf., volume 2, pages 413–422, 1995.Google Scholar
  2. 2.
    C. Bregler and J. Malik. Tracking people with twists and exponential maps. In Proc. CVPR, 1998.Google Scholar
  3. 3.
    J. Deutscher, A. Blake, B. North, and B. Bascle. Tracking through singularities and discontinuities by random sampling. In Proc. 7th Int. ConJ. on Computer Vision, volume 2, pages 1144–1149, 1999.Google Scholar
  4. 4.
    J. Deutscher, A. Blake, and I. Reid. Articulated body motion capture by annealed particle filtering. In Proc. Conf. Computer Vision and Pattern Recognition, volume 2, pages 1144–1149, 2000.Google Scholar
  5. 5.
    D. Gavrila and L.S. Davis. 3d model-based tracking of humans in action: a multi-view approach. Proc. Conf. Computer Vision and Pattern Recognition, pages 73–80, 1996.Google Scholar
  6. 6.
    I. Haritaoglu, D. Harwood, and L. Davis. w 4 s: A real-time system for detecting and tracking people in 2.5D. In Proc. 5th European Conf. Computer Vision, volume 1, pages 877–892, Freiburg, Germany, June 1998. Springer Verlag.Google Scholar
  7. 7.
    C. G. Harris. Tracking with rigid models. In A. Blake and A. Yuille, editors, Active Vision. MIT Press, Cambridge, MA, 1992.Google Scholar
  8. 8.
    Nicholas R. Howe, Michael E. Leventon, and William T. Freeman. Bayesian reconstruction of 3D human motion from single-camera video. In Advances in Neural Information Processing Systems 12, pages 82–26. MIT Press, 2000.Google Scholar
  9. 9.
    M.A. Isard and A. Blake. Visual tracking by stochastic propagation of conditional density. In Proc. 4th European Conf. Computer Vision, pages 343–356, Cambridge, England, Apr 1996.Google Scholar
  10. 10.
    S.X. Ju, M.J. Black, and Y. Yacoob. Cardboard people: A parameterized model of articulated motion. In 2nd Int. Conf. on Automatic Face and Gesture Recognition, Killington, Vermont, pages 38–44, 1996.Google Scholar
  11. 11.
    D.G. Lowe. Robust model-based motion tracking through the integration of search and estimation. Int. J. Computer Vision, 8(2): 113–122, 1992.CrossRefGoogle Scholar
  12. 12.
    J. MacCormick and A. Blake. Partitioned sampling, articulated objects and interface-quality hand tracking. In Accepted to ECCV 2000, 2000.Google Scholar
  13. 13.
    D. Reynard, A.P. Wildenberg, A. Blake, and J. Marchant. Learning dynamics of complex motions from image sequences. In Proc. 4th European Conf. Computer Vision. pages 357–368, Cambridge, England, Apr 1996.Google Scholar
  14. 14.
    H. Sidenbladh. M. J. Black, and D. J. Fleet. Stochastic tracking of 3D human figures using 2d image motion. In Proceedings of the 6th European Conference on Computer Vision, Dublin, 2000.Google Scholar
  15. 15.
    R. A. Smith. A. W. Fitzgibbon, and A. Zisserman. Improving augmented reality using image and scene constraints. In Proc. 10th British Machine Vision Conference, Nottingham. pages 295–304. BMVA Press, 1999.Google Scholar
  16. 16.
    Vicon web based literature. URL,2001.Google Scholar

Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Andrew J. Davison
    • 1
  • Jonathan Deutscher
    • 1
  • Ian D. Reid
    • 1
  1. 1.Robotics Research Group Department of Engineering ScienceUniversity of OxfordOxfordUK

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