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The Process of Motion Capture: Dealing with the Data

  • Bobby Bodenheimer
  • Chuck Rose
  • Seth Rosenthal
  • John Pella
Part of the Eurographics book series (EUROGRAPH)

Abstract

This paper presents a detailed description of the process of motion capture, whereby sensor information from a performer is transformed into an articulated, hierarchical rigid-body object. We describe the gathering of the data, the real-time construction of a virtual skeleton which a director can use for immediate feedback, and the offline processing which produces the articulated object. This offline process involves a robust statistical estimation of the size of the skeleton and an inverse kinematic optimization to produce the desired joint angle trajectories. Additionally, we discuss a variation on the inverse kinematic optimization which can be used when the standard approach does not yield satisfactory results for the special cases when joint angle consistency is desired between a group of motions. These procedures work well and have been used to produce motions for a number of commercial games.

Keywords

Joint Angle Motion Capture Inverse Kinematic Motion Capture Data Vector Constraint 
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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References

  1. [1]
    Badler, N. I., Hollick, M. J., and Granieri, J. P. Real-time control of a virtual human using minimal sensors. Presence 2, 1 (1993), 82–86.Google Scholar
  2. [2]
    Badler, N. I., Phillips, C. B., and Webber, B. L. Simulating Humans: Computer Graphics Animation and Control. Oxford University Press, Oxford, UK, 1993.MATHGoogle Scholar
  3. [3]
    Bruderlin, A., and Williams, L. Motion signal processing. In Computer Graphics (Aug. 1995), pp. 97–104. Proceedings of SIGGRAPH 95.Google Scholar
  4. [4]
    Gill, P. E., Murray, W., and Wright, M. H. Practical Optimization. Academic Press, 1981.MATHGoogle Scholar
  5. [5]
    Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., and Stahel, W. A. Robust Statistics: The Approach Based on Influence Functions. John H. Wiley, New York, 1986.Google Scholar
  6. [6]
    Hars, A. Masters of motion. Computer Graphics World (Oct. 1996), 26–34.Google Scholar
  7. [7]
    Hodgins, J. K., Wooten, W. L., Brogan, D. C., and O’brien, J. F. Animating human athletics. In Computer Graphics (Aug. 1995), pp. 71–78. Proceedings of SIGGRAPH 95.Google Scholar
  8. [8]
    Houy, D. R. Range of motion in college males. Presented at the Conference of the Human Factors Society, Santa Monica, CA, 1983.Google Scholar
  9. [9]
    Maestri, G. Capturing motion. Computer Graphics World (1995), 47–51.Google Scholar
  10. [10]
    Maiocchi, R. 3-D character animation using motion capture. In Interactive Computer Animation, N. Magnetat-Thalmann and D. Thalmann, Eds. Prentice-Hall, London, 1996, pp. 10–39.Google Scholar
  11. [11]
    Maurel, W., Thalmann, D., Hoffmeyer, P., Beylot, P., Gingins, P., Kalra, P., and Thalmann, N. M. A biomechanical musculoskeletal model of human upper limb for dynamic simulation. In Computer Animation and Simulation ’96 (Aug. 1996), R. Boulic and G. Hégron, Eds., pp. 121–136.CrossRefGoogle Scholar
  12. [12]
    Molet, T., Boulic, R., and Thalmann, D. A real time anatomical converter for human motion capture. In Computer Animation and Simulation ’96 (Aug. 1996), R. Boulic and G. Hégron, Eds., pp. 79–94.CrossRefGoogle Scholar
  13. [13]
    Perlin, K. Real time responsive animation with personality. IEEE Transactions on Visualization and Computer Graphics 1, 1(Mar. 1995), 5–15.CrossRefGoogle Scholar
  14. [14]
    Rose, C. F., Guenter B., Bodenheimer, B., and Cohen, M. Efficientgeneration of motion transitions using spacetime constraints. In Computer Graphics (Aug. 1996), pp. 147–154. Proceedings of SIGGRAPH 96.Google Scholar
  15. [15]
    Witkin, A., and Popović, Z. Motion warping. In Computer Graphics (Aug. 1995), pp. 105–108. Proceedings of SIGGRAPH 95.Google Scholar
  16. [16]
    Zhao, J., and Badler, N.I. Inverse kinematics positioning using non-linear programming for highly articulated figures. ACM Trans. Gr. 13, 4 (Oct. 1994), 313–336.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag/Wien 1997

Authors and Affiliations

  • Bobby Bodenheimer
    • 1
    • 2
  • Chuck Rose
    • 1
    • 2
  • Seth Rosenthal
    • 1
    • 3
  • John Pella
    • 1
    • 3
  1. 1.One Microsoft WayRedmodeUSA
  2. 2.Microsoft ResearchUSA
  3. 3.Interactive Media ProductionMicrosoftUSA

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