article

Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces

Authors:

Jessica K. Hodgins,

Nancy S. PollardAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 23, Issue 3

Pages 514 - 521

https://doi.org/10.1145/1015706.1015754

Published: 01 August 2004 Publication History

Abstract

Optimization is an appealing way to compute the motion of an animated character because it allows the user to specify the desired motion in a sparse, intuitive way. The difficulty of solving this problem for complex characters such as humans is due in part to the high dimensionality of the search space. The dimensionality is an artifact of the problem representation because most dynamic human behaviors are intrinsically low dimensional with, for example, legs and arms operating in a coordinated way. We describe a method that exploits this observation to create an optimization problem that is easier to solve. Our method utilizes an existing motion capture database to find a low-dimensional space that captures the properties of the desired behavior. We show that when the optimization problem is solved within this low-dimensional subspace, a sparse sketch can be used as an initial guess and full physics constraints can be enabled. We demonstrate the power of our approach with examples of forward, vertical, and turning jumps; with running and walking; and with several acrobatic flips.

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Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 23, Issue 3

August 2004

684 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1015706

Editor:
John C. Hart

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Copyright © 2004 ACM.

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Publication History

Published: 01 August 2004

Published in TOG Volume 23, Issue 3

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Cited By

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Yang ZWen YChen SLiu XGao YLiu YGao LFu H(2024)Keyframe Control of Music-Driven 3D Dance GenerationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.323553830:7(3474-3486)Online publication date: Jul-2024
https://doi.org/10.1109/TVCG.2023.3235538
Mir APuig XKanazawa APons-Moll G(2024)Generating Continual Human Motion in Diverse 3D Scenes2024 International Conference on 3D Vision (3DV)10.1109/3DV62453.2024.00061(903-913)Online publication date: 18-Mar-2024
https://doi.org/10.1109/3DV62453.2024.00061
Valova IGueorguieva NMai TChen R(2023)Fuzzy clustering in high-dimensional spaces: Visualization and performance metricsInternational Journal of Knowledge-based and Intelligent Engineering Systems10.3233/KES-221614(1-21)Online publication date: 21-Dec-2023
https://doi.org/10.3233/KES-221614
Lee SKang TPark JLee JWon J(2023)SAME: Skeleton-Agnostic Motion Embedding for Character AnimationSIGGRAPH Asia 2023 Conference Papers10.1145/3610548.3618206(1-11)Online publication date: 10-Dec-2023
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Ye YLiu LHu LXia S(2023)Neural3Points: Learning to Generate Physically Realistic Full‐body Motion for Virtual Reality UsersComputer Graphics Forum10.1111/cgf.1463441:8(183-194)Online publication date: 20-Mar-2023
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