Learning Physics-Based Motion Style with Nonlinear Inverse Optimization (2005)
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| Venue: | ACM Trans. Graph |
| Citations: | 75 - 12 self |
BibTeX
@ARTICLE{Liu05learningphysics-based,
author = {C. Karen Liu and Aaron Hertzmann and Zoran Popovic},
title = {Learning Physics-Based Motion Style with Nonlinear Inverse Optimization},
journal = {ACM Trans. Graph},
year = {2005},
volume = {24},
pages = {1071--1081}
}
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Abstract
This paper presents a novel physics-based representation of realistic character motion. The dynamical model incorporates several factors of locomotion derived from the biomechanical literature, including relative preferences for using some muscles more than others, elastic mechanisms at joints due to the mechanical properties of tendons, ligaments, and muscles, and variable stiffness at joints depending on the task. When used in a spacetime optimization framework, the parameters of this model define a wide range of styles of natural human movement.







