Article

Behavior planning for character animation

Authors:

James J. KuffnerAuthors Info & Claims

SCA '05: Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation

Pages 271 - 280

https://doi.org/10.1145/1073368.1073408

Published: 29 July 2005 Publication History

Abstract

This paper explores a behavior planning approach to automatically generate realistic motions for animated characters. Motion clips are abstracted as high-level behaviors and associated with a behavior finite-state machine (FSM) that defines the movement capabilities of a virtual character. During runtime, motion is generated automatically by a planning algorithm that performs a global search of the FSM and computes a sequence of behaviors for the character to reach a user-designated goal position. Our technique can generate interesting animations using a relatively small amount of data, making it attractive for resource-limited game platforms. It also scales efficiently to large motion databases, because the search performance is primarily dependent on the complexity of the behavior FSM rather than on the amount of data. Heuristic cost functions that the planner uses to evaluate candidate motions provide a flexible framework from which an animator can control character preferences for certain types of behavior. We show results of synthesized animations involving up to one hundred human and animal characters planning simultaneously in both static and dynamic environments.

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Cited By

Liu TLiu ZWang YChai Y(2024)Modeling quick autonomous response for virtual characters in safety education gamesCognitive Systems Research10.1016/j.cogsys.2024.10127688(101276)Online publication date: Dec-2024
https://doi.org/10.1016/j.cogsys.2024.101276
Polykretis IPatil AAanjaneya MMichmizos K(2023)An Interactive Framework for Visually Realistic 3D Motion Synthesis using Evolutionarily-trained Spiking Neural NetworksProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/35855096:1(1-19)Online publication date: 16-May-2023
https://dl.acm.org/doi/10.1145/3585509
Zhao YJiang JChen YLiu RYang YXue XChen S(2022)Metaverse: Perspectives from graphics, interactions and visualizationVisual Informatics10.1016/j.visinf.2022.03.0026:1(56-67)Online publication date: Mar-2022
https://doi.org/10.1016/j.visinf.2022.03.002
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Index Terms

Behavior planning for character animation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
        3D imaging
  2. Computer graphics
    1. Animation

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Published In

cover image ACM Conferences

SCA '05: Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation

July 2005

366 pages

ISBN:1595931988

DOI:10.1145/1073368

Conference Chairs:
Demetri Terzopoulos
New York University, USA
,
Victor Brian Zordan
University of California at Riverside, USA
,
Program Chairs:
Ken Anjyo
OLM Digital, Inc., Japan
,
Petros Faloutsos
University of California at Los Angeles, USA

Copyright © 2005 ACM.

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]

Sponsors

SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
EUROGRAPHICS: The European Association for Computer Graphics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 July 2005

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SCA05

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SIGGRAPH
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SCA05: Symposium on Computer Animation

July 29 - 31, 2005

California, Los Angeles

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Overall Acceptance Rate 183 of 487 submissions, 38%

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Cited By

Liu TLiu ZWang YChai Y(2024)Modeling quick autonomous response for virtual characters in safety education gamesCognitive Systems Research10.1016/j.cogsys.2024.10127688(101276)Online publication date: Dec-2024
https://doi.org/10.1016/j.cogsys.2024.101276
Polykretis IPatil AAanjaneya MMichmizos K(2023)An Interactive Framework for Visually Realistic 3D Motion Synthesis using Evolutionarily-trained Spiking Neural NetworksProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/35855096:1(1-19)Online publication date: 16-May-2023
https://dl.acm.org/doi/10.1145/3585509
Zhao YJiang JChen YLiu RYang YXue XChen S(2022)Metaverse: Perspectives from graphics, interactions and visualizationVisual Informatics10.1016/j.visinf.2022.03.0026:1(56-67)Online publication date: Mar-2022
https://doi.org/10.1016/j.visinf.2022.03.002
Ao SZhou TLong GLu QZhu LJiang JRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)CO-PILOTProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3541060(10444-10456)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3541060
Joo SManzoor SRocha YBae SLee KKuc TKim M(2020)Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment ModelingApplied Sciences10.3390/app1009321910:9(3219)Online publication date: 5-May-2020
https://doi.org/10.3390/app10093219
Park GSohn CLee Y(2020)Motion generation using Center of MassJournal of the Korea Computer Graphics Society10.15701/kcgs.2020.26.2.1126:2(11-19)Online publication date: 1-Jun-2020
https://doi.org/10.15701/kcgs.2020.26.2.11
Huang WTerzopoulos D(2020)Door and Doorway Etiquette for Virtual HumansIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2018.287405026:3(1502-1517)Online publication date: 1-Mar-2020
https://doi.org/10.1109/TVCG.2018.2874050
Althoff MMayer MMuller R(2020)Automatic Synthesis of Human Motion from Temporal Logic Specifications2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS45743.2020.9341666(4040-4046)Online publication date: 24-Oct-2020
https://doi.org/10.1109/IROS45743.2020.9341666
Wang XJiang XRegedzai GMeng HSun L(2020)Gated neural network framework for interactive character controlMultimedia Tools and Applications10.1007/s11042-020-08792-yOnline publication date: 13-Mar-2020
https://doi.org/10.1007/s11042-020-08792-y
Eysenbach BSalakhutdinov RLevine SWallach HLarochelle HBeygelzimer Ad'Alché-Buc FFox E(2019)Search on the replay bufferProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3455653(15246-15257)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3454287.3455653
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