N-body spacetime constraints

Animators frequently choreograph complex motions for multiple objects that interact through collision and obstruction. In such situations, the use of physically based dynamics to confer visual realism creates challenging computational problems. Typically forward simulation is well understood, but the inverse problem of motion synthesis—that of synthesizing motions consistent both with physical law and with the animator's requirements—is generally tedious and sometimes intractable. We show how N-body inverse problems can be formulated as optimization tasks. We present a simply stated, but combinatorially formidable example that exhibits all of the essential sources of complexity common to N-body motion synthesis, and show how it can be solved approximately using heuristic methods based on evolutionary computation.

[1]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[2]  C. Boldrighini,et al.  Billiards in Polygons , 1978 .

[3]  J. D. Schaffer,et al.  Multiple Objective Optimization with Vector Evaluated Genetic Algorithms , 1985, ICGA.

[4]  J. Smillie,et al.  A rational billiard flow is uniquely ergodic in almost every direction , 1985 .

[5]  Andrew P. Witkin,et al.  Spacetime constraints , 1988, SIGGRAPH.

[6]  Arun N. Netravali,et al.  Motion interpolation by optimal control , 1988, SIGGRAPH.

[7]  Jane Wilhelms,et al.  Collision Detection and Response for Computer Animation , 1988, SIGGRAPH.

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  James K. Hahn,et al.  Realistic animation of rigid bodies , 1988, SIGGRAPH.

[10]  Gunar E. Liepins,et al.  Some Guidelines for Genetic Algorithms with Penalty Functions , 1989, ICGA.

[11]  David Baraff,et al.  Analytical methods for dynamic simulation of non-penetrating rigid bodies , 1989, SIGGRAPH.

[12]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[13]  J. Doug Tygar,et al.  The computability and complexity of optical beam tracing , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

[14]  Alan H. Barr,et al.  Geometric collisions for time-dependent parametric surfaces , 1990, SIGGRAPH.

[15]  David Baraff,et al.  Coping with friction for non-penetrating rigid body simulation , 1991, SIGGRAPH.

[16]  BaraffDavid Coping with friction for non-penetrating rigid body simulation , 1991 .

[17]  Nostrand Reinhold,et al.  the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .

[18]  B. Huberman,et al.  Chaos, qualitative reasoning, and the predictability problem , 1993 .

[19]  Michiel van de Panne,et al.  Sensor-actuator networks , 1993, SIGGRAPH.

[20]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[21]  C. Fonseca,et al.  GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION , 1993 .

[22]  Joe Marks,et al.  Physically Realistic Motion Synthesis in Animation , 1993, Evolutionary Computation.

[23]  Joe Marks,et al.  Spacetime constraints revisited , 1993, SIGGRAPH.

[24]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.