Advertisement

Motion Editing with the State Feedback Dynamic Model

  • Dengming Zhu
  • Zhaoqi Wang
  • Shihong Xia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4035)

Abstract

In this paper, a novel motion editing tool, called the state feedback dynamic model, is proposed and demonstrated for the animators to edit the pre-existing motion capture data. The state feedback dynamic model is based on the linear time-invariant system (LTI). Compared with previous works, by this model, the animators need only modify a few keyframes manually, and the other frames can be adjusted automatically while preserving as much of the original quality as possible. It is a global modification on motion sequence. More important, the LTI model derives an explicit mapping between the high-dimensional motion capture data and low-dimensional hidden state variables. It transforms a number of possibly correlated joint angle variables into a smaller number of uncorrelated state variables. Then, the motion sequence is edited in state space, and which considers that the motion among joints is correlated. It is different from traditional methods which consider each joint as independent of each other. Finally, an effective algorithm is also developed to calculate the model parameters. Experimental results show that the generated animations through this method are natural and smooth.

Keywords

Motion Sequence Dynamic Texture Motion Capture Data Original Motion Intermediate Frame 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bruderlin, A., Williams, L.: Motion signal processing. In: Proceedings of ACM SIGGRAPH 1995, pp. 97–104 (1995)Google Scholar
  2. 2.
    Witkin, A., Popovic, Z.: Motion Warping. Computer Graphics (SIGGRAPH 1995) 29(4), 105–108 (1995)Google Scholar
  3. 3.
    Li, Y., Wang, T., Shum, H.-Y.: Motion Texture: A Two-Level Statistical Model for Character Motion Synthesis. ACM Transactions on Graphics 21(3), 465–472 (2002)CrossRefGoogle Scholar
  4. 4.
    Gleicher, M.: Comparing constraint-based motion editing methods. Graph Models 63, 107–134 (2001)MATHCrossRefGoogle Scholar
  5. 5.
    Unuma, M., Anjyo, K., Takeuchi, R.: Fourier principles for emotion-based human figure animation. In: Proceedings of ACM SIGGRAPH 1995, pp. 91–96 (1995)Google Scholar
  6. 6.
    Pullen, K., Bregler, C.: Motion Capture Assisted Animation: Texturing and Synthesis. In: Proc. SIGGRAPH 2002, pp. 501–508 (2002)Google Scholar
  7. 7.
    Witkin, A., Kass, M.: Spacetime constraints. Computer Graphics (SIGGRAPH 1988) 22, 159–168 (1988)CrossRefGoogle Scholar
  8. 8.
    Cohen, M.F.: Interactive spacetime control for animation. In: SIGGRAPH 1992, pp. 293–302 (1992)Google Scholar
  9. 9.
    Liu, Z., Gortler, S.J., Cohen, M.F.: Hierarchical Spacetime Control. In: SIGGRAPH 1993, pp. 35–42 (1993)Google Scholar
  10. 10.
    Gleicher, M.: Retargetting motion to new characters. In: SIGGRAPH 1998, vol. 32, pp. 33–42 (1998)Google Scholar
  11. 11.
    Gleicher, M.: Motion Editing with Spacetime Constraints. In: Proceedings of the 1997 Symposium on Interactive 3D Graphics, pp. 139–148 (1997)Google Scholar
  12. 12.
    Lee, J., Shin, S.Y.: A hierarchical approach to interactive motion editing for human-like figures. In: SIGGRAPH 1999, pp. 39–48 (1999)Google Scholar
  13. 13.
    Soatto, S., Doretto, G., Wu, Y.N.: Dynamic textures. In: IEEE International Conference on Computer Vision, pp. 439–446 (2001)Google Scholar
  14. 14.
    Hsu, E., Pulli, K., Popovi, J.: Style translation for human motion. ACM Transactions on Graphics (TOG) 24(3), 1082–1089 (2005)CrossRefGoogle Scholar
  15. 15.
    Ghahramani, Z., Hinton, G.E.: Parameter estimation for linear dynamical systems. University of Toronto Technical Report CRG-TR-96-2, 6 pages (1996)Google Scholar
  16. 16.
    Lee, J., Chai, J., Reitsma, P., Hodgins, J., Pollard, N.: Interactive Control of Avatars Animated with Human Motion Data. ACM Transactions on Graphics 21(3), 491–500 (2002)Google Scholar
  17. 17.
    Grassia, F.S.: Practical parameterization of rotations using the exponential map. Journal of Graphics Tools 3(3), 29–48 (1998)Google Scholar
  18. 18.
    Nocedal, J., Wright, S.: Numerical Optimization. Springer, Heidelberg (1999)MATHCrossRefGoogle Scholar
  19. 19.
    Franklin, G.F., Powell, J.D., Emami-Naeini, A.: Feedback Control of Dynamic Systems, 4th edn. Prentice-Hall, Englewood Cliffs (2002)Google Scholar
  20. 20.
    Kovar, L., Schreiner, J., Gleicher, M.: Footskate cleanup for motion capture editing. In: Proceedings of ACM SIGGRAPH Symposium on Computer Animation, pp. 97–104 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dengming Zhu
    • 1
    • 2
  • Zhaoqi Wang
    • 1
  • Shihong Xia
    • 1
  1. 1.Institute of Computing TechnologyChinese Academy of Sciences 
  2. 2.Graduate School of the Chinese Academy of SciencesBeijingChina

Personalised recommendations