“Compact character controllers” – ACM SIGGRAPH HISTORY ARCHIVES

“Compact character controllers”

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Title:

    Compact character controllers

Session/Category Title:   Character animation


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Abstract:


    We present methods for creating compact and efficient data-driven character controllers. Our first method identifies the essential motion data examples tailored for a given task. It enables complex yet efficient high-dimensional controllers, as well as automatically generated connecting controllers that merge a set of independent controllers into a much larger aggregate one without modifying existing ones. Our second method iteratively refines basis functions to enable highly complex value functions. We show that our methods dramatically reduce the computation and storage requirement of controllers and enable very complex behaviors.

References:


    1. Beaudoin, P., van de Panne, M., and Poulin, P. 2007. Automatic construction of compact motion graphs. Tech. Rep. 1296, Universite de Montreal, May. DIRO.Google Scholar
    2. Beaudoin, P., van de Panne, M., Poulin, P., and Coros, S. 2008. Motion-motif graphs. In Symposium on Computer Animation 2008, ACM. Google ScholarDigital Library
    3. Bellman, R. E. 1957. Dynamic Programming. Princeton University Press. Google ScholarDigital Library
    4. Choi, M. G., Ju, E., Chang, J., Kim, Y. J., and Lee, J. 2009. Linkless octree using multi-level perfect hashing. Pacific Graphics 2009.Google Scholar
    5. Cooper, S., Hertzmann, A., and Popović, Z. 2007. Active learning for real-time motion controllers. ACM Transactions on Graphics 26, 3 (July), 5. Google ScholarDigital Library
    6. Ikemoto, L., Arikan, O., and Forsyth, D. 2005. Learning to move autonomously in a hostile environment. Tech. Rep. UCB/CSD-5-1395, University of California at Berkeley, June.Google Scholar
    7. Keller, P. W., Mannor, S., and Precup, D. 2006. Automatic basis function construction for approximate dynamic programming and reinforcement learning. In ICML ’06: Proceedings of the 23rd international conference on Machine learning, ACM, New York, NY, USA, 449–456. Google ScholarDigital Library
    8. Kovar, L., and Gleicher, M. 2004. Automated extraction and parameterization of motions in large data sets. ACM Transactions on Graphics 23, 3. Google ScholarDigital Library
    9. Lagoudakis, M. G., and Parr, R. 2003. Least-squares policy iteration. Journal of Machine Learning Research 4, 1107–1149. Google ScholarDigital Library
    10. Lamouret, A., and van de Panne, M. 1996. Motion synthesis by example. In In EGCAS 96: Seventh International Workshop on Computer Animation and Simulation, Eurographics, 199–212. Google ScholarDigital Library
    11. Lau, M., and Kuffner, J. J. 2006. Precomputed search trees: Planning for interactive goal-driven animation. In Proceedings of the 2006 ACM SIGGRAPH / Eurographics Symposium on Computer Animation, 299–308. Google ScholarDigital Library
    12. Lee, J., and Lee, K. H. 2004. Precomputing avatar behavior from human motion data. In Proceedings of the 2004 ACM SIGGRAPH / Eurographics Symposium on Computer Animation, ACM Press, 79–87. Google ScholarDigital Library
    13. Liu, K., Hertzmann, A., and Popović, Z. 2005. Learning physics-based motion style with nonlinear inverse optimization. ACM Transactions on Graphics 24, 3, 1071–1081. Google ScholarDigital Library
    14. Lo, W.-Y., and Zwicker, M. 2008. Real-time planning for parameterized human motion. In 2008 ACM SIGGRAPH / Eurographics Symposium on Computer Animation, 29–38. Google ScholarDigital Library
    15. Mahadevan, S., and Maggioni, M. 2006. Proto-value functions: A laplacian framework for learning representation and control in markov decision processes. Tech. Rep. TR-2006-36, University of Massachusetts, Department of Computer Science.Google Scholar
    16. McCann, J., and Pollard, N. 2007. Responsive characters from motion fragments. ACM Transactions on Graphics 26, 3 (July), 6. Google ScholarDigital Library
    17. Moore, A. 1991. Variable resolution dynamic programming: Efficiently learning action maps in multivariate real-valued statespaces. In Machine Learning: Proceedings of the Eighth International Conference, L. Birnbaum and G. Collins, Eds.Google ScholarCross Ref
    18. Muico, U., Lee, Y., Popović, J., and Popović, Z. 2009. Contact-aware nonlinear control of dynamic characters. ACM Transactions on Graphics 28, 3. Google ScholarDigital Library
    19. Munos, R., and Moore, A. 2002. Variable resolution discretization in optimal control. Machine Learning 49, 2–3, 291–323. Google ScholarDigital Library
    20. Reitsma, P., and Pollard, N. 2007. Evaluating motion graphs for character animation. ACM Transactions on Graphics 26, 4 (Oct.), 18. Google ScholarDigital Library
    21. Treuille, A., Lee, Y., and Popović, Z. 2007. Near-optimal character animation with continuous control. ACM Transactions on Graphics 26, 3 (July), 7. Google ScholarDigital Library
    22. Zhao, L., Normoyle, A., Khanna, S., and Safonova, A. 2009. Automatic construction of a minimum size motion graph. In Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation. Google ScholarDigital Library


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