Physically-Based Character Control in Low Dimensional Space

  • Hubert P. H. Shum
  • Taku Komura
  • Takaaki Shiratori
  • Shu Takagi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6459)


In this paper, we propose a new method to compose physically-based character controllers in low dimensional latent space. Source controllers are created by gradually updating the task parameter such as the external force applied to the body. During the optimization, instead of only saving the optimal controllers, we also keep a large number of non-optimal controllers. These controllers provide knowledge about the stable area in the controller space, and are then used as samples to construct a low dimensional manifold that represents stable controllers. During run-time, we interpolate controllers in the low dimensional space and create stable controllers to cope with the irregular external forces. Our method is best to be applied for real-time applications such as computer games.


Character animation dynamics simulation dimensionality reduction controller interpolation 


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  1. 1.
    Armstrong, H.G.: Anthropometry and mass distribution for human analogues. Military male aviators, vol. 1 (1988)Google Scholar
  2. 2.
    Coros, S., Beaudoin, P., van de Panne, M.: Generalized biped walking control. In: SIGGRAPH 2010: ACM SIGGRAPH 2010 Papers, pp. 1–9. ACM, New York (2010)Google Scholar
  3. 3.
    De Silva, V., Tenenbaum, J.B.: Global versus local methods in nonlinear dimensionality reduction. In: Advances in Neural Information Processing Systems 15, vol. 15, pp. 705–712 (2003)Google Scholar
  4. 4.
    Grochow, K., Martin, S.L., Hertzmann, A., Popović, Z.: Style-based inverse kinematics. In: SIGGRAPH 2004: ACM SIGGRAPH 2004 Papers, pp. 522–531. ACM, New York (2004)Google Scholar
  5. 5.
    Hansen, N.: The cma evolution strategy: A comparing review. In: Towards a New Evolutionary Computation, Studies in Fuzziness and Soft Computing, vol. 192, pp. 75–102. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Hansen, N., Müller, S.D., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es). Evol. Comput. 11(1), 1–18 (2003)CrossRefGoogle Scholar
  7. 7.
    Kwon, T., Hodgins, J.: Control systems for human running using an inverted pendulum model and a reference motion capture sequence. In: SCA 2010: Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer animation. Eurographics Association, Aire-la-Ville, Switzerland (2010)Google Scholar
  8. 8.
    de Lasa, M., Mordatch, I., Hertzmann, A.: Feature-based locomotion controllers. ACM Trans. Graph. 29(4), 1–10 (2010)CrossRefGoogle Scholar
  9. 9.
    Lawrence, N.D.: Gaussian process latent variable models for visualisation of high dimensional data. In: NIPS, p. 2004 (2004)Google Scholar
  10. 10.
    Lee, Y., Kim, S., Lee, J.: Data-driven biped control. ACM Trans. Graph. 29(4), 1–8 (2010)Google Scholar
  11. 11.
    Mordatch, I., de Lasa, M., Hertzmann, A.: Robust physics-based locomotion using low-dimensional planning. ACM Trans. Graph. 29(4), 1–8 (2010)CrossRefGoogle Scholar
  12. 12.
    Pratt, J.E., Tedrake, R.: Velocity-based stability margins for fast bipedal walking, vol. 340, pp. 299–324 (2006)Google Scholar
  13. 13.
    Shin, H.J., Lee, J.: Motion synthesis and editing in low-dimensional spaces: Research articles. Comput. Animat. Virtual Worlds 17(3-4), 219–227 (2006)CrossRefGoogle Scholar
  14. 14.
    da Silva, M., Abe, Y., Popović, J.: Interactive simulation of stylized human locomotion. In: SIGGRAPH 2008: ACM SIGGRAPH 2008 Papers, pp. 1–10. ACM, New York (2008)Google Scholar
  15. 15.
    Smith, R.: Open dynamics engine,
  16. 16.
    Tenenbaum, J.B., Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science (5500), 2319–2323 (December)Google Scholar
  17. 17.
    Tsai, Y.Y., Lin, W.C., Cheng, K.B., Lee, J., Lee, T.Y.: Real-time physics-based 3d biped character animation using an inverted pendulum model. IEEE Transactions on Visualization and Computer Graphics (2009)Google Scholar
  18. 18.
    Van De Panne, M., Lamouret, A.: Guided optimization for balanced locomotion. In: Terzopoulos, D., Thalmann, D. (eds.) 6th Eurographics Workshop on Animation and Simulation, Computer Animation and Simulation, Eurographics, pp. 165–177. Springer, Wien (September 1995)Google Scholar
  19. 19.
    Wampler, K., Popović, Z.: Optimal gait and form for animal locomotion. ACM Trans. Graph. 28(3), 1–8 (2009)CrossRefGoogle Scholar
  20. 20.
    Wang, J.M., Fleet, D.J., Hertzmann, A.: Optimizing walking controllers. In: SIGGRAPH Asia 2009: ACM SIGGRAPH Asia 2009 Papers, pp. 1–8. ACM, New York (2009)Google Scholar
  21. 21.
    Wang, J.M., Fleet, D.J., Hertzmann, A.: Optimizing walking controllers for uncertain inputs and environments. ACM Trans. Graph. 29(4), 1–8 (2010)Google Scholar
  22. 22.
    Yin, K., Coros, S., Beaudoin, P., van de Panne, M.: Continuation methods for adapting simulated skills. In: SIGGRAPH 2008: ACM SIGGRAPH 2008 Papers, pp. 1–7. ACM, New York (2008)Google Scholar
  23. 23.
    Yin, K., Loken, K., van de Panne, M.: Simbicon: Simple biped locomotion control. ACM Trans. Graph. 26(3), Article 105 (2007)Google Scholar
  24. 24.
    Young, F.W.: Multidimensional scaling. In: Kotz-Johnson Encyclopedia of Statistical Sciences, vol. 5, John Wiley & Sons, Inc., Chichester (1985)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hubert P. H. Shum
    • 1
  • Taku Komura
    • 2
  • Takaaki Shiratori
    • 3
  • Shu Takagi
    • 1
  1. 1.RIKENSaitamaJapan
  2. 2.Edinburgh UniversityUnited Kingdom
  3. 3.Carnegie Mellon UniversityPittsburghUSA

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