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Crowd art: density and flow based crowd motion design

Authors:

Panayiotis Charalambous,

Julien Pettré,

Marie-Paule CaniAuthors Info & Claims

MIG '15: Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games

Pages 167 - 176

https://doi.org/10.1145/2822013.2822023

Published: 16 November 2015 Publication History

Abstract

Artists, animation and game designers are in demand for solutions to easily populate large virtual environments with crowds that satisfy desired visual features. This paper presents a method to intuitively populate virtual environments by specifying two key features: localized density, being the amount of agents per unit of surface, and localized flow, being the direction in which agents move through a unit of surface. The technique we propose is also time-independant, meaning that whatever the time in the animation, the resulting crowd satisfies both features. To achieve this, our approach relies on the Crowd Patches model. After discretizing the environment into regular patches and creating a graph that links these patches, an iterative optimization process computes the local changes to apply on each patch (increasing/reducing the number of agents in each patch, updating the directions of agents in the patch) in order to satisfy overall density and flow constraints. A specific stage is then introduced after each iteration to avoid the creation of local loops by using a global pathfinding process. As a result, the method has the capacity of generating large realistic crowds in minutes that endlessly satisfy both user specified densities and flow directions, and is robust to contradictory inputs. At last, to ease the design the method is implemented in an artist-driven tool through a painting interface.

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Cited By

Yan DDing GHuang KHuang T(2024)Generating natural pedestrian crowds by learning real crowd trajectories through a transformer-based GANThe Visual Computer10.1007/s00371-024-03385-4Online publication date: 29-Apr-2024
https://doi.org/10.1007/s00371-024-03385-4
Lemonari MBlanco RCharalambous PPelechano NAvraamides MPettré JChrysanthou Y(2022)Authoring Virtual Crowds: A SurveyComputer Graphics Forum10.1111/cgf.1450641:2(677-701)Online publication date: 24-May-2022
https://doi.org/10.1111/cgf.14506
Colas Avan Toll WZibrek KHoyet LOlivier APettré J(2022)Interaction Fields: Intuitive Sketch‐based Steering Behaviors for Crowd SimulationComputer Graphics Forum10.1111/cgf.1449141:2(521-534)Online publication date: 24-May-2022
https://doi.org/10.1111/cgf.14491
Show More Cited By

Index Terms

Crowd art: density and flow based crowd motion design
1. Computing methodologies
  1. Computer graphics
    1. Animation

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cover image ACM Other conferences

MIG '15: Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games

November 2015

247 pages

ISBN:9781450339919

DOI:10.1145/2822013

Copyright © 2015 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]

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 November 2015

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Research-article

Funding Sources

French National Research Agency ANR

Conference

MIG '15

MIG '15: Motion in Games

November 16 - 18, 2015

Paris, France

Acceptance Rates

Overall Acceptance Rate -9 of -9 submissions, 100%

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Cited By

Yan DDing GHuang KHuang T(2024)Generating natural pedestrian crowds by learning real crowd trajectories through a transformer-based GANThe Visual Computer10.1007/s00371-024-03385-4Online publication date: 29-Apr-2024
https://doi.org/10.1007/s00371-024-03385-4
Lemonari MBlanco RCharalambous PPelechano NAvraamides MPettré JChrysanthou Y(2022)Authoring Virtual Crowds: A SurveyComputer Graphics Forum10.1111/cgf.1450641:2(677-701)Online publication date: 24-May-2022
https://doi.org/10.1111/cgf.14506
Colas Avan Toll WZibrek KHoyet LOlivier APettré J(2022)Interaction Fields: Intuitive Sketch‐based Steering Behaviors for Crowd SimulationComputer Graphics Forum10.1111/cgf.1449141:2(521-534)Online publication date: 24-May-2022
https://doi.org/10.1111/cgf.14491
Wu YZhang LDing GYan DZhang F(2021)TSN: Performance Creative Choreography Based on Twin Sensor NetworkWireless Communications and Mobile Computing10.1155/2021/55327542021(1-12)Online publication date: 26-Mar-2021
https://doi.org/10.1155/2021/5532754
Ricks BDobson AKrontiris ABekris KKapadia MRoberts F(2019)Generation of crowd arrival and destination locations/times in complex transit facilitiesThe Visual Computer10.1007/s00371-019-01761-zOnline publication date: 14-Oct-2019
https://doi.org/10.1007/s00371-019-01761-z
Montana LMaddock SWan TVidal F(2017)Sketching for real-time control of crowd simulationsProceedings of the Conference on Computer Graphics & Visual Computing10.2312/cgvc.20171282(81-88)Online publication date: 14-Sep-2017
https://dl.acm.org/doi/10.2312/cgvc.20171282

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