“Near-optimal character animation with continuous control” by Treuille, Lee and Popovic

We present a new approach to realtime character animation with interactive control. Given a corpus of motion capture data and a desired task, we automatically compute near-optimal controllers using a low-dimensional basis representation. We show that these controllers produce motion that fluidly responds to several dimensions of user control and environmental constraints in realtime. Our results indicate that very few basis functions are required to create high-fidelity character controllers which permit complex user navigation and obstacle-avoidance tasks.

1. Arikan, O., and Forsyth, D. A. 2002. Interactive motion generation from examples. ACM Transactions on Graphics 21, 3 (July), 483–490. Google ScholarDigital Library
2. Arikan, O., Forsyth, D. A., and O’Brien, J. F. 2003. Motion synthesis from annotations. ACM Transactions on Graphics 22, 3 (July), 402–408. Google ScholarDigital Library
3. Atkeson, C. G., Moore, A. W., and Schaal, S. 1997. Locallyweighted learning for control. Artificial Intelligence Review 11, 1–5 (Feb.), 75–113. Google ScholarDigital Library
4. Bertsekas, D. P. 2001. Dynamic Programming and Optimal Control, vol. 2. Athena Scientific. Google ScholarDigital Library
5. Choi, M. G., Lee, J., and Shin, S. Y. 2003. Planning biped locomotion using motion capture data and probabilistic roadmaps. ACM Transactions on Graphics 22, 2 (Apr.), 182–203. Google ScholarDigital Library
6. De Farias, D. P. 2002. The Linear Programming Approach to Approximate Dynamic Programming: Theory and Application. PhD thesis, Stanford. Google ScholarDigital Library
7. Gleicher, M., Shin, H. J., Kovar, L., and Jepsen, A. 2003. Snap-together motion: Assembling run-time animation. ACM Transactions on Graphics 22, 3 (July), 702–702. Google ScholarDigital Library
8. Ikemoto, L., Arikan, O., and Forsyth, D. 2005. Learning to move autonomously in a hostile environment. Tech. Rep. UCB/CSD-5-1395, June.Google Scholar
9. Kaelbling, L. P., Littman, M. L., and Moore, A. W. 1996. Reinforcement learning: A survey. Journal of Artifcial Intelligence Research 4 (May), 237–285. Google ScholarDigital Library
10. Kovar, L., Gleicher, M., and Pighin, F. 2002. Motion graphs. ACM Transactions on Graphics 21, 3 (July), 473–482. Google ScholarDigital Library
11. Kwon, T., and Shin, S. Y. 2005. Motion modeling for on-line locomotion synthesis. In 2005 ACM SIGGRAPH / Eurographics Symposium on Computer Animation, 29–38. Google ScholarDigital Library
12. Lau, M., and Kuffner, J. J. 2006. Precomputed search trees: Planning for interactive goal-driven animation. In 2006 ACM SIGGRAPH / Eurographics Symposium on Computer Animation, 299–308. Google ScholarDigital Library
13. LaValle, S. M. 2006. Planning Algorithms. Cambridge University Press. Google ScholarDigital Library
14. Lee, J., and Lee, K. H. 2004. Precomputing avatar behavior from human motion data. In 2004 ACM SIGGRAPH / Eurographics Symposium on Computer Animation, 79–87. Google ScholarDigital Library
15. Lee, J., Chai, J., Reitsma, P. S. A., Hodgins, J. K., and Pollard, N. S. 2002. Interactive control of avatars animated with human motion data. ACM Transactions on Graphics 21, 3 (July), 491–500. Google ScholarDigital Library
16. Lee, K. H., Choi, M. G., and Lee, J. 2006. Motion patches: building blocks for virtual environments annotated with motion data. ACM Transactions on Graphics 25, 3 (July), 898–906. Google ScholarDigital Library
17. McCann, J., and Pollard, N. 2007. Responsive characters from motion fragments. ACM Transactions on Graphics (SIGGRAPH 2007) 26, 3 (July). To appear. Google ScholarDigital Library
18. Moore, A. W., and Atkeson, C. G. 1995. The parti-game algorithm for variable resolution reinforcement learning in multidimensional state-spaces. Machine Learning 21. Google ScholarDigital Library
19. Pullen, K., and Bregler, C. 2002. Motion capture assisted animation: Texturing and synthesis. ACM Transactions on Graphics 21, 3 (July), 501–508. Google ScholarDigital Library
20. Safonova, A., and Hodgins, J. K. 2005. Analyzing the physical correctness of interpolated human motion. In In Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation, 171–180. Google ScholarDigital Library
21. Safonova, A., and Hodgins, J. K. 2007. Construction and optimal search of interpolated motion graphs. ACM Transactions on Graphics (SIGGRAPH) 26, 3 (July). To appear. Google ScholarDigital Library
22. Schödl, A., and Essa, I. 2001. Machine learning for video-based rendering. Advances in Neural Information Processing Systems 13 (July), 1002–1008.Google Scholar
23. Shin, H. J., and Oh, H. S. 2006. Fat graphs: Constructing an interactive character with continuous controls. In 2006 ACM SIGGRAPH / Eurographics Symposium on Computer Animation, 291–298. Google ScholarDigital Library
24. Singh, S. P., and Yee, R. C. 1994. An upper bound on the loss from approximate optimal-value functions. Machine Learning 16, 4 (September), 227–233. Google ScholarDigital Library
25. Trick, M. A., and Zin, S. E. 1997. Spline approximations to value functions: A linear programming approach. Macroeconomic Dynamics 1, 255277.Google ScholarCross Ref