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Related Experiment Videos

An inverse dynamics approach to face animation.

M Pitermann1, K G Munhall

  • 1Department of Psychology, Queen's University, Kingston, Ontario, Canada. mpiter@psyc.queensu.ca

The Journal of the Acoustical Society of America
|September 27, 2001
PubMed
Summary

This study introduces a method to create realistic facial animation from movement data, bypassing the need for direct muscle signal recordings. The dynamic inversion technique successfully generates high-quality facial animations from kinematic data.

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Area of Science:

  • Computer Graphics
  • Biomechanical Modeling
  • Human-Computer Interaction

Background:

  • Muscle-based facial models offer high-quality animation but require complex muscle signal inputs.
  • Current methods for obtaining muscle signals often involve trial-and-error, limiting efficiency.

Purpose of the Study:

  • To develop a dynamic inversion method for muscle-based facial animation.
  • To enable facial animation generation directly from kinematic recordings of facial movements.

Main Methods:

  • A dynamic inversion of a muscle-based facial model was performed using Powell's algorithm.
  • Seven lower-face muscles' activity was calculated to minimize error between OPTOTRAK kinematic data and the facial mesh.
  • The inverted muscle activity was used to drive the facial animation.

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Main Results:

  • Strong correlations were found between animation kinematics and 3D OPTOTRAK data across different adaptation scenarios.
  • High-quality facial animation was achieved, closely matching recorded facial movements.
  • The inversion process demonstrated robustness across different talkers and model adaptations.

Conclusions:

  • The dynamic inversion method effectively generates facial animation from kinematic data.
  • While the inversion is ill-posed for electromyography (EMG) prediction, it provides accurate kinematic outputs.
  • The motor system's redundancy allows for diverse muscle activation patterns to produce similar facial movements.