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Hierarchical spacetime control
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Source International Conference on Computer Graphics and Interactive Techniques archive
Proceedings of the 21st annual conference on Computer graphics and interactive techniques table of contents
Pages: 35 - 42  
Year of Publication: 1994
ISBN:0-89791-667-0
Authors
Zicheng Liu  Department of Computer Science, Princeton University
Steven J. Gortler  Department of Computer Science, Princeton University
Michael F. Cohen  Department of Computer Science, Princeton University
Sponsor
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 11,   Downloads (12 Months): 52,   Citation Count: 42
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ABSTRACT

Specifying the motion of an animated linked figure such that it achieves given tasks (e.g., throwing a ball into a basket) and performs the tasks in a realistic fashion (e.g., gracefully, and following physical laws such as gravity) has been an elusive goal for computer animators. The spacetime constraints paradigm has been shown to be a valuable approach to this problem, but it suffers from computational complexity growth as creatures and tasks approach those one would like to animate. The complexity is shown to be, in part, due to the choice of finite basis with which to represent the trajectories of the generalized degrees of freedom. This paper describes new features to the spacetime constraints paradigm to address this problem.The functions through time of the generalized degrees of freedom are reformulated in a hierarchical wavelet representation. This provides a means to automatically add detailed motion only where it is required, thus minimizing the number of discrete variables. In addition the wavelet basis is shown to lead to better conditioned systems of equations and thus faster convergence.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Chui, C., and Quak, E. Wavelets on a bounded interval. Nu-merical Methods of Approximation Theory 9 (1992), 53-75.
 
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Fletcher, R. Practical Methods of Optimization, vol. 1. John Wiley and Sons, 1980.
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Gortler, S., and Cohen, M. F. Variational modeling with wavelets. Tech. Rep. CS-TR-456-94, Department of Computer Sci-ence, Princeton University, 1994.
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Papalambros, P. Y., and Wilde, D. J. Principles of Optimal Design. Cambridge University Press, Cambridge, England, 1988.
 
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Quak, E., and Weyrich, N. Decomposition and reconstruction algorithms for spline wavelets on a bounded interval. Tech. Rep. 294, Center for Approximation Theory, Texas A&M, 1993.
 
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Rall, L. B. Automatic Differentiation: Techniques and Applica-tions. Springer-Verlag, 1981.
 
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CITED BY  42

Collaborative Colleagues:
Zicheng Liu: colleagues
Steven J. Gortler: colleagues
Michael F. Cohen: colleagues