Article

Group motion graphs

Authors:

Stephen Chenney,

ShaoHua FanAuthors Info & Claims

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

Pages 281 - 290

https://doi.org/10.1145/1073368.1073409

Published: 29 July 2005 Publication History

Abstract

We introduce Group Motion Graphs, a data-driven animation technique for groups of discrete agents, such as flocks, herds, or small crowds. Group Motion Graphs are conceptually similar to motion graphs constructed from motion-capture data, but have some important differences: we assume simulated motion; transition nodes are found by clustering group configurations from the input simulations: and clips to join transitions are explicitly constructed via constrained simulation. Graphs built this way offer known bounds on the trajectories that they generate, making it easier to search for particular output motions. The resulting animations show realistic motion at significantly reduced computational cost compared to simulation, and improved control.

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Cited By

Charalambous PPettre JVassiliades VChrysanthou YPelechano N(2023)GREIL-Crowds: Crowd Simulation with Deep Reinforcement Learning and ExamplesACM Transactions on Graphics10.1145/359245942:4(1-15)Online publication date: 26-Jul-2023
https://dl.acm.org/doi/10.1145/3592459
Panayiotou AKyriakou TLemonari MChrysanthou YCharalambous P(2022)CCP: Configurable Crowd ProfilesACM SIGGRAPH 2022 Conference Proceedings10.1145/3528233.3530712(1-10)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.1145/3528233.3530712
Ren JXiang WXiao YYang RManocha DJin X(2021)Heter-Sim: Heterogeneous Multi-Agent Systems Simulation by Interactive Data-Driven OptimizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.294676927:3(1953-1966)Online publication date: 1-Mar-2021
https://doi.org/10.1109/TVCG.2019.2946769
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Index Terms

Group motion graphs
1. Computing methodologies
  1. Computer graphics
    1. Animation

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Information & Contributors

Information

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

Sponsor:

SIGGRAPH
EUROGRAPHICS

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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Citations

Cited By

Charalambous PPettre JVassiliades VChrysanthou YPelechano N(2023)GREIL-Crowds: Crowd Simulation with Deep Reinforcement Learning and ExamplesACM Transactions on Graphics10.1145/359245942:4(1-15)Online publication date: 26-Jul-2023
https://dl.acm.org/doi/10.1145/3592459
Panayiotou AKyriakou TLemonari MChrysanthou YCharalambous P(2022)CCP: Configurable Crowd ProfilesACM SIGGRAPH 2022 Conference Proceedings10.1145/3528233.3530712(1-10)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.1145/3528233.3530712
Ren JXiang WXiao YYang RManocha DJin X(2021)Heter-Sim: Heterogeneous Multi-Agent Systems Simulation by Interactive Data-Driven OptimizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.294676927:3(1953-1966)Online publication date: 1-Mar-2021
https://doi.org/10.1109/TVCG.2019.2946769
Musse SCassol VThalmann D(2021)A history of crowd simulation: the past, evolution, and new perspectivesThe Visual Computer10.1007/s00371-021-02252-wOnline publication date: 5-Aug-2021
https://doi.org/10.1007/s00371-021-02252-w
Yoo IFišer MHu KBenes B(2020)Character motion in function spaceThe Visual Computer10.1007/s00371-020-01840-6Online publication date: 4-Apr-2020
https://doi.org/10.1007/s00371-020-01840-6
Zhou BLiu XPeng SZhong BDu J(2017)Efficient Human Motion Transition via Hybrid Deep Neural Network and Reliable Motion Graph MiningComputer Vision10.1007/978-981-10-7299-4_60(717-728)Online publication date: 30-Nov-2017
https://doi.org/10.1007/978-981-10-7299-4_60
Palachano NAllbeck JKapadia MBadler N(2016)Learning Heterogeneous Crowd Behavior from the Real WorldSimulating Heterogeneous Crowd with Interactive Behaviors10.1201/9781315370071-4(43-60)Online publication date: 19-Oct-2016
https://doi.org/10.1201/9781315370071-4
Jordao KCharalambous PChristie MPettré JCani M(2015)Crowd artProceedings of the 8th ACM SIGGRAPH Conference on Motion in Games10.1145/2822013.2822023(167-176)Online publication date: 16-Nov-2015
https://dl.acm.org/doi/10.1145/2822013.2822023
Wang SGain JNistchke GEsparcia-Alcázar A(2015)Controlling Crowd Simulations using Neuro-EvolutionProceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739480.2754715(353-360)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739480.2754715
Hwang JSuh IKwon T(2014)Editing and Synthesizing Two-Character Motions using a Coupled Inverted Pendulum ModelComputer Graphics Forum10.1111/cgf.1247033:7(21-30)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1111/cgf.12470
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