Expressive features for movement exaggeration

Authors:
James W. Davis;Vignesh S. Kannappan
Affiliations:
Ohio State University;Ohio State University
Venue:
ACM SIGGRAPH 2002 conference abstracts and applications
Year:
2002

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Abstract

Given a single motion-capture sequence of a person performing a dynamic activity at a particular intensity (or effort), our goal is to automatically warp that movement into a natural-looking exaggerated version of that action. Consider warping a movement of a person lifting a lightweight box to make the movement appear as if the box were actually very heavy. We describe an efficient data-driven approach applicable to animation re-use that learns the underlying regularity in an action to select the most "expressive" features to exaggerate. Other "style-based" approaches are presented in [Gleicher 1998; Brand and Hertzmann 2000; Vasilescu 2001].