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Computational Visual Media 2019, 5(2): 171-191 https://doi.org/10.1007/s41095-019-0138-z
Research Article | Open Access | Issue | Published: 13 April 2019

Optimal and interactive keyframe selection for motion capture

Show Author's Information Richard Roberts1( ),  J. P. Lewis2,  Ken Anjyo1,3,  Jaewoo Seo4,  Yeongho Seol5
Victoria University of Wellington, Wellington, New Zealand.
SEED, Electronic Arts, Los Angeles, United States.
OLM Digital, Tokyo, Japan.
Pinscreen, Los Angeles, United States.
Weta Digital, Wellington, New Zealand.
Keywords:
motion capture, motion editing, keyframe animation, dynamic programming
Cite this article:
Roberts R, Lewis JP, Anjyo K, et al. Optimal and interactive keyframe selection for motion capture. Computational Visual Media, 2019, 5(2): 171-191. https://doi.org/10.1007/s41095-019-0138-z
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Abstract Full text Electronic supplementary material About this article

Motion capture is increasingly used in games and movies, but often requires editing before it can be used, for many reasons. The motion may need to be adjusted to correctly interact with virtual objects or to fix problems that result from mapping the motion to a character of a different size or, beyond such technical requirements, directors can request stylistic changes. Unfortunately, editing is laborious because of the low-level representation of the data. While existing motion editing methods accomplish modest changes, larger edits can require the artist to “re-animate” the motion by manually selecting a subset of the frames as keyframes. In this paper, we automatically find sets of frames to serve as keyframes for editing the motion. We formulate the problem of selecting an optimal set of keyframes as a shortest-path problem, and solve it efficiently using dynamic programming. We create a new simplified animation by interpolating the found keyframes using a naive curve fitting technique. Our algorithm can simplify motion capture to around 10% of the original number of frames while retaining most of its detail. By simplifying animation with our algorithm, we realize a new approach to motion editing and stylization founded on the time-tested keyframe interface. We present results that show our algorithm outperforms both research algorithms and a leading commercial tool.


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About this article

Optimal and interactive keyframe selection for motion capture

Show Author's information Richard Roberts1( ), J. P. Lewis2, Ken Anjyo1,3, Jaewoo Seo4, Yeongho Seol5
Victoria University of Wellington, Wellington, New Zealand.
SEED, Electronic Arts, Los Angeles, United States.
OLM Digital, Tokyo, Japan.
Pinscreen, Los Angeles, United States.
Weta Digital, Wellington, New Zealand.

Abstract

Motion capture is increasingly used in games and movies, but often requires editing before it can be used, for many reasons. The motion may need to be adjusted to correctly interact with virtual objects or to fix problems that result from mapping the motion to a character of a different size or, beyond such technical requirements, directors can request stylistic changes. Unfortunately, editing is laborious because of the low-level representation of the data. While existing motion editing methods accomplish modest changes, larger edits can require the artist to “re-animate” the motion by manually selecting a subset of the frames as keyframes. In this paper, we automatically find sets of frames to serve as keyframes for editing the motion. We formulate the problem of selecting an optimal set of keyframes as a shortest-path problem, and solve it efficiently using dynamic programming. We create a new simplified animation by interpolating the found keyframes using a naive curve fitting technique. Our algorithm can simplify motion capture to around 10% of the original number of frames while retaining most of its detail. By simplifying animation with our algorithm, we realize a new approach to motion editing and stylization founded on the time-tested keyframe interface. We present results that show our algorithm outperforms both research algorithms and a leading commercial tool.

Keywords: motion capture, motion editing, keyframe animation, dynamic programming

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Publication history

Revised: 13 December 2018
Accepted: 15 February 2019
Published: 13 April 2019
Issue date: June 2019

Copyright

© The author(s) 2019

Acknowledgements

Many researchers and artists have contributed important insights to this research. The authors would like to give special thanks to Ayumi Kimura and other staff of OLM Digital, to Johan Andersson, Ida Winterhaven, and Binh Le of SEED, Electronic Arts, and also to Ian Loh and other staff of Victoria University of Wellington’s Computational Media Innovation Centre and Virtual Worlds Lab. The authors would also like to thank the Moveshelf team for supporting the web-based presentation of our results.

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