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Generalizing motion edits with Gaussian processes

Published: 09 February 2009 Publication History

Abstract

One way that artists create compelling character animations is by manipulating details of a character's motion. This process is expensive and repetitive. We show that we can make such motion editing more efficient by generalizing the edits an animator makes on short sequences of motion to other sequences. Our method predicts frames for the motion using Gaussian process models of kinematics and dynamics. These estimates are combined with probabilistic inference. Our method can be used to propagate edits from examples to an entire sequence for an existing character, and it can also be used to map a motion from a control character to a very different target character. The technique shows good generalization. For example, we show that an estimator, learned from a few seconds of edited example animation using our methods, generalizes well enough to edit minutes of character animation in a high-quality fashion. Learning is interactive: An animator who wants to improve the output can provide small, correcting examples and the system will produce improved estimates of motion. We make this interactive learning process efficient and natural with a fast, full-body IK system with novel features. Finally, we present data from interviews with professional character animators that indicate that generalizing and propagating animator edits can save artists significant time and work.

Supplementary Material

JPG File (ikemoto1.jpg)
JPG File (ikemoto2.jpg)
ikemoto1 (ikemoto1.mov)
1st supplemental movie file for Generalizing motion edits with Gaussian processes
ikemoto2 (ikemoto2.mov)
2nd supplemental movie file for Generalizing motion edits with Gaussian processes

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Published In

cover image ACM Transactions on Graphics
ACM Transactions on Graphics  Volume 28, Issue 1
January 2009
144 pages
ISSN:0730-0301
EISSN:1557-7368
DOI:10.1145/1477926
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 09 February 2009
Accepted: 01 August 2008
Received: 01 August 2007
Published in TOG Volume 28, Issue 1

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Author Tags

  1. Artist-guided content creation
  2. controllable motion editing

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  • (2024)MotionDiffuse: Text-Driven Human Motion Generation With Diffusion ModelIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.335541446:6(4115-4128)Online publication date: 29-Jan-2024
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