article

Learning physics-based motion style with nonlinear inverse optimization

Authors:

Aaron Hertzmann,

Zoran PopovićAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 24, Issue 3

Pages 1071 - 1081

https://doi.org/10.1145/1073204.1073314

Published: 01 July 2005 Publication History

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.Due to the complexity of biological motion, these style parameters are too difficult to design by hand. To address this, we introduce Nonlinear Inverse Optimization, a novel algorithm for estimating optimization parameters from motion capture data. Our method can extract the physical parameters from a single short motion sequence. Once captured, this representation of style is extremely flexible: motions can be generated in the same style but performing different tasks, and styles may be edited to change the physical properties of the body.

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Index Terms

Learning physics-based motion style with nonlinear inverse optimization
1. Computing methodologies
  1. Computer graphics
    1. Animation

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 24, Issue 3

July 2005

826 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1073204

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Copyright © 2005 ACM.

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Publication History

Published: 01 July 2005

Published in TOG Volume 24, Issue 3

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Wang ZBenes BQureshi AMousas C(2024)Evolution-Based Shape and Behavior Co-Design of Virtual AgentsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.335574530:12(7579-7591)Online publication date: 1-Dec-2024
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Ji BPan YYan YChen RYang X(2024)StyleVR: Stylizing Character Animations With Normalizing FlowsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.325918330:7(4183-4196)Online publication date: 1-Jul-2024
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