Ho, ESL, Wang, H orcid.org/0000-0002-2281-5679 and Komura, T (2014) A multi-resolution approach for adapting close character interaction. In: Proceedings. 20th ACM Symposium on Virtual Reality Software and Technology (VRST 14), 11-13 Nov 2014, Edinburgh, Scotland. ACM , pp. 97-106. ISBN 978-1-4503-3253-8
Abstract
Synthesizing close interactions such as dancing and fighting between characters is a challenging problem in computer animation. While encouraging results are presented in [Ho et al. 2010], the high computation cost makes the method unsuitable for interactive motion editing and synthesis. In this paper, we propose an efficient multiresolution approach in the temporal domain for editing and adapting close character interactions based on the Interaction Mesh framework. In particular, we divide the original large spacetime optimization problem into multiple smaller problems such that the user can observe the adapted motion while playing-back the movements during run-time. Our approach is highly parallelizable, and achieves high performance by making use of multi-core architectures. The method can be applied to a wide range of applications including motion editing systems for animators and motion retargeting systems for humanoid robots.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in VRST '14 Proceedings of the 20th ACM Symposium on Virtual Reality Software and Technology, http://doi.acm.org/10.1145/2671015.2671020. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Character animation, close interaction, spacetime constraints |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Institute for Computational and Systems Science (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Nov 2016 11:46 |
Last Modified: | 17 Jan 2018 04:23 |
Published Version: | https://doi.org/10.1145/2671015.2671020 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/2671015.2671020 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:106110 |