Abstract
We present a physics-based approach to synthesizing motion of a virtual character in a dynamically varying environment. Our approach views the motion of a responsive virtual character as a sequence of solutions to the constrained optimization problem formulated at every time step. This framework allows the programmer to specify active control strategies using intuitive kinematic goals, significantly reducing the engineering effort entailed in active body control. Our optimization framework can incorporate changes in the character's surroundings through a synthetic visual sensory system and create significantly different motions in response to varying environmental stimuli. Our results show that our approach is general enough to encompass a wide variety of highly interactive motions.
Supplemental Material
- Abe, Y., da Silva, M., and Popović, J. 2007. Multiobjective control with frictional contacts. In Proceedings of the Eurographics/SIGGRAPH Symposium on Computer Animation, 249--258. Google ScholarDigital Library
- Abe, Y. and Popović, J. 2006. Interactive animation of dynamic manipulation. In Proceedings of the Eurographics/SIGGRAPH Symposium on Computer Animation. Google ScholarDigital Library
- Cohen, M. F. 1992. Interactive spacetime control for animation. In SIGGRAPH. Vol. 26, 293--302. Google ScholarDigital Library
- da Silva, M., Abe, Y., and Popovic, J. 2008. Simulation of human motion data using short-horizon model-predictive control. Comput. Graphics Forum (EUROGRAPHICS) 27, 2, 371--380.Google ScholarCross Ref
- Faloutsos, P., van de Panne, M., and Terzopoulos, D. 2001. Composable controllers for physics-based character animation. SIGGRAPH, 251--260. Google ScholarDigital Library
- Fang, A. C. and Pollard, N. S. 2003. Efficient synthesis of physically valid human motion. ACM Trans. Graphics, 417--426. Google ScholarDigital Library
- Gill, P., Saunders, M., and Murray, W. 1996. Snopt: An SQP algorithm for large-scale constrained optimization. Tech. rep. NA 96-2, University of California, San Diego.Google Scholar
- Hodgins, J. K., Wooten, W. L., Brogan, D. C., and O'Brien, J. F. 1995. Animating human athletics. SIGGRAPH, 71--78. Google ScholarDigital Library
- Isaacs, P. M. and Cohen, M. F. 1987. Controlling dynamic simulation with kinematic constraints. SIGGRAPH, 215--224. Google ScholarDigital Library
- Kawato, M. 1999. Internal models for motor control and trajectory planning. In Current Opinions in Neurobiology, Vol. 9.Google ScholarCross Ref
- Kudoh, S., Komura, T., and Ikeuchi, K. 2006. Stepping motion for a human-like character to maintain balance against large perturbations. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2661--2666.Google Scholar
- Laszlo, J., van de Panne, M., and Fiume, E. 1996. Limit cycle control and its application to the animation of balancing and walking. SIGGRAPH, 155--162. Google ScholarDigital Library
- Liegeois, A. 1977. Automatic supervisory control of the configuration and behavior of multibody mechanisms. IEEE Trans. Syst. Man Cybernetics 7, 12, 868--871.Google ScholarCross Ref
- Liu, C. K. 2008. Synthesis of interactive hand manipulation. In Proceedings of the Eurographics/SIGGRAPH Symposium on Computer Animation. Google ScholarDigital Library
- Liu, C. K., Hertzmann, A., and Popović, Z. 2005. Learning physics-based motion style with nonlinear inverse optimization. ACM Trans. Graphics 24, 3, 1071--1081. Google ScholarDigital Library
- Liu, C. K. and Popović, Z. 2002. Synthesis of complex dynamic character motion from simple animations. ACM Trans. Graphics 21, 3, 408--416. Google ScholarDigital Library
- Liu, Z., Gortler, S. J., and Cohen, M. F. 1994. Hierarchical spacetime control. SIGGRAPH, 35--42. Google ScholarDigital Library
- Lockhart, D. B. and Ting, L. H. 2007. Optimal sensorimotor transformations for balance. Nat Neurosci 10, 1329--1336.Google ScholarCross Ref
- Maciejewski, A. A. and Klein, C. A. 1985. Obstacle avoidance for kinematically redundant manipulators in dynamically varying environments. Int. J. Robotics Res. 4, 3, 109--117.Google ScholarCross Ref
- Metoyer, R., Zordan, V., Hermens, B., Wu, C.-C., and Soriano, M. 2008. Psychologically inspired anticipation and dynamic response for impacts to the head and upper body. IEEE Trans. Visualization Comput. Graphics 14, 1, 173--185. Google ScholarDigital Library
- NaturalMotion. 2006. Endorphin. www.naturalmotion.com.Google Scholar
- Popović, Z. and Witkin, A. 1999. Physically based motion transformation. SIGGRAPH, 11--20. Google ScholarDigital Library
- Raibert, M. H. 1986. Legged Robots That Balance. Massachusetts Institute of Technology, Cambridge, Massachusetts. Google ScholarDigital Library
- Safonova, A., Hodgins, J. K., and Pollard, N. S. 2004. Synthesizing physically realistic human motion in low-dimensinal, behavior-specific spaces. ACM Trans. Graphics 23, 3, 514--521. Google ScholarDigital Library
- Sentis, L. and Khatib, O. 2005. Synthesis of whole-body behaviors through hierarchical control of behavioral primitives. Int. J. Humanoid Robotics 2, 4, 505--518.Google ScholarCross Ref
- Sentis, L. and Khatib, O. 2006. A whole-body control framework for humanoids operating in human environments. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). 2641--2648.Google Scholar
- Sharon, D. and van de Panne, M. 2005. Synthesis of controllers for stylized planar bipedal walking. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).Google Scholar
- Stewart, A. J. and Cremer, J. F. 1992a. Animation of 3d human locomotion: Climbing stairs and descending stairs. In Proceedings of the Eurographics Workshop on Animation and Simulation, 152--168.Google Scholar
- Stewart, A. J. and Cremer, J. F. 1992b. Beyond keyframing: An algorithmic approach to animation. In Graphics Interface, 273--281. Google ScholarDigital Library
- Sulejmanpašić, A. and Popović, J. 2004. Adaptation of performed ballistic motion. ACM Trans. Graphics 24, 1. Google ScholarDigital Library
- Uno, Y., Kawato, M., and Suzuki, R. 1989. Minimum muscle-tension-change model which reproduces human arm movement. In Proceedings of the Symposium on Biological and Physiological Engineering, 299--302.Google Scholar
- van de Panne, M. and Lamouret, A. 1995. Guided optimization for balanced locomotion. In Computer Animation and Simulation, 165--177.Google Scholar
- Witkin, A. and Kass, M. 1988. Spacetime constraints. SIGGRAPH. 22, 159--168. Google ScholarDigital Library
- Wooten, W. L. 1998. Simulation of leaping, tumbling, landing, and balancing humans. Ph.D. thesis, Georgia Institute of Technology. Google ScholarDigital Library
- Yamane, K. and Nakamura, Y. 2000. Dynamics filter?Concept and implementation of on-line motion generator for human figures. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 688--695.Google Scholar
- Yin, K., Loken, K., and van de Panne, M. 2007. Simbicon: simple biped locomotion control. ACM Trans. Graphics 26, 3, 105. Google ScholarDigital Library
- Zordan, V., Macchietto, A., Medin, J., Soriano, M., Wu, C.-C., Metoyer, R., and Rose, R. 2007. Anticipation from example. In Proceedings of the ACM Symposium on Virtual Reality Software and Technology (VRST'07). 81--84. Google ScholarDigital Library
- Zordan, V. B. and Hodgins, J. K. 1999. Tracking and modifying upper-body human motion data with dynamic simulation. In Conference on Computer Animation and Simulation.Google Scholar
Index Terms
- Optimization-based interactive motion synthesis
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