On Implicit Modeling for Fitting Purposes

  • Ralf Plänkers
  • Pascal Fua
Part of the IFIP — The International Federation for Information Processing book series (IFIPAICT, volume 68)

Abstract

Tracking and modeling people from video sequences has become an increasingly important research topic, with applications including animation, surveillance and sports medicine. In this paper, we propose a model based 3—D approach to recovering both body shape and motion. It takes advantage of a sophisticated animation model to achieve both robustness and realism. Stereo sequences of people in motion serve as input to our system. From these, we extract a 2.5—D description of the scene and, optionally, silhouette edges. We propose an integrated framework to fit the model and to track the person’s motion. Constraints for 3—D points and silhouette edges are presented in detail. We recover not only the motion but also a full animation model closely resembling the subject.

Keywords

Body fitting body modeling implicit surface metaball 

References

  1. Baerlocher, P. and Boulic, R. (1999). Inverse kinematics report. Technical report, EPFL—DI—LIG.Google Scholar
  2. Blanc, C. and Schlick, C. (1995). Extended field functions for soft objects. In Eurographics Workshop on Implicit Surfaces 95, pages 21–32, Grenoble, France.Google Scholar
  3. Fua, P. (1995). Reconstructing Complex Surfaces from Multiple Stereo Views. In International Conference on Computer Vision, pages 1078–1085, Cambridge, MA.CrossRefGoogle Scholar
  4. Fua, P. (1999). Using Model-Driven Bundle-Adjustment to Model Heads from Raw Video Sequences. In International Conference on Computer Vision,Corfu, Greece.Google Scholar
  5. Gavrila, D. (1999). The Visual Analysis of Human Movement: A Survey. Computer Vision and Image Understanding, 73 (1).Google Scholar
  6. Hilton, A., Beresford, D., Gentils, T., Smith, R., and Sun, W. (1999). Virtual People: Capturing Human Models to Populate Virtual Worlds. In Computer Animation,Geneva, Switzerland.Google Scholar
  7. Konolige, K. (1997). Small Vision Systems: Hardware and Implementation. In Eighth International Symposium on Robotics Research,Hayama, Japan.Google Scholar
  8. Lee, W., Gu, J., and Thalmann, N. M. (2000). Generating animatable 3d virtual humans from photographs. In Computer Graphics forum, Eurographics, volume 19, pages C1 — C10, Interlaken, Switzerland.Google Scholar
  9. Moeslund, T. and Granum, E. (2001). A Survey of Computer Vision-Based Human Motion Capture. Computer Vision and Image Understanding, 81 (3).Google Scholar
  10. Plänkers, R. and Fua, P. (2001). Articulated Soft Objects for Video-based Body Mod- eling. In International Conference on Computer Vision,Vancouver, Canada.Google Scholar
  11. Press, W., Flannery, B., Teukolsky, S., and Vetterling, W. (1986). Numerical Recipes, the Art of Scientific Computing. Cambridge U. Press, Cambridge, MA.Google Scholar
  12. Sullivan, S., Sandford, L., and Ponce, J. (1994). Using Geometric Distance Fits for 3—D. Object Modeling and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16 (12): 1183–1196.CrossRefGoogle Scholar
  13. Thalmann, D., Shen, J., and Chauvineau, E. (1996). Fast Realistic Human Body Deformations for Animation and VR Applications. In Computer Graphics International,Pohang, Korea.Google Scholar

Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Ralf Plänkers
    • 1
  • Pascal Fua
    • 1
  1. 1.Computer Graphics Lab (LIG)Swiss Federal Institute of Technology (EPFL)LausanneSwitzerland

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