short-paper

Data-Driven Crowd Simulation with Generative Adversarial Networks

Authors:

Wouter van Toll,

Jean-Bernard Hayet,

Julien PettréAuthors Info & Claims

CASA '19: Proceedings of the 32nd International Conference on Computer Animation and Social Agents

Pages 7 - 10

https://doi.org/10.1145/3328756.3328769

Published: 01 July 2019 Publication History

Abstract

This paper presents a novel data-driven crowd simulation method that can mimic the observed traffic of pedestrians in a given environment. Given a set of observed trajectories, we use a recent form of neural networks, Generative Adversarial Networks (GANs), to learn the properties of this set and generate new trajectories with similar properties. We define a way for simulated pedestrians (agents) to follow such a trajectory while handling local collision avoidance. As such, the system can generate a crowd that behaves similarly to observations, while still enabling real-time interactions between agents. Via experiments with real-world data, we show that our simulated trajectories preserve the statistical properties of their input. Our method simulates crowds in real time that resemble existing crowds, while also allowing insertion of extra agents, combination with other simulation methods, and user interaction.

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Cited By

Zou XYan YHao XHu YWen HLiu EZhang JLi YLi TZheng YLiang Y(2025)Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlookInformation Fusion10.1016/j.inffus.2024.102606113(102606)Online publication date: Jan-2025
https://doi.org/10.1016/j.inffus.2024.102606
Cheng CLi JDastani MSichman JAlechina NDignum V(2024)ODEs Learn to Walk: ODE-Net based Data-Driven Modeling for Crowd DynamicsProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662883(345-353)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662883
Yan DDing GHuang KBai CHe LZhang L(2024)Enhanced Crowd Dynamics Simulation with Deep Learning and Improved Social Force ModelElectronics10.3390/electronics1305093413:5(934)Online publication date: 29-Feb-2024
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Index Terms

Data-Driven Crowd Simulation with Generative Adversarial Networks
1. Computing methodologies

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Published In

cover image ACM Other conferences

CASA '19: Proceedings of the 32nd International Conference on Computer Animation and Social Agents

July 2019

95 pages

ISBN:9781450371599

DOI:10.1145/3328756

Copyright © 2019 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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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: 01 July 2019

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CASA '19

CASA '19: Computer Animation and Social Agents

July 1 - 3, 2019

Paris, France

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Overall Acceptance Rate 18 of 110 submissions, 16%

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Cited By

Zou XYan YHao XHu YWen HLiu EZhang JLi YLi TZheng YLiang Y(2025)Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlookInformation Fusion10.1016/j.inffus.2024.102606113(102606)Online publication date: Jan-2025
https://doi.org/10.1016/j.inffus.2024.102606
Cheng CLi JDastani MSichman JAlechina NDignum V(2024)ODEs Learn to Walk: ODE-Net based Data-Driven Modeling for Crowd DynamicsProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662883(345-353)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662883
Yan DDing GHuang KBai CHe LZhang L(2024)Enhanced Crowd Dynamics Simulation with Deep Learning and Improved Social Force ModelElectronics10.3390/electronics1305093413:5(934)Online publication date: 29-Feb-2024
https://doi.org/10.3390/electronics13050934
Martin JHoyet LPinsard EPaillat JPettré J(2024)Virtual Crowds Rheology: Evaluating the Effect of Character Representation on User Locomotion in CrowdsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345618330:11(7008-7019)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456183
Nakazawa SKato Y(2024)Environment Classification Method Using Autoencoder to Select Appropriate Crowd Model for Robot Simulation2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)10.1109/CASE59546.2024.10711425(1877-1882)Online publication date: 28-Aug-2024
https://doi.org/10.1109/CASE59546.2024.10711425
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
Chao QLiu PHan YLin YLi CMiao QJin X(2023)A Calibrated Force-Based Model for Mixed Traffic SimulationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.312828629:3(1664-1677)Online publication date: 1-Mar-2023
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Kothari PAlahi A(2023)Safety-Compliant Generative Adversarial Networks for Human Trajectory ForecastingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.323390624:4(4251-4261)Online publication date: Apr-2023
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Lin XLiang YZhang YHu YYin B(2023)IE-GAN: a data-driven crowd simulation method via generative adversarial networksMultimedia Tools and Applications10.1007/s11042-023-17346-x83:15(45207-45240)Online publication date: 20-Oct-2023
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Yan DHuang KZhang LDing G(2023)Stylized Crowd Formation Transformation Through Spatiotemporal Adversarial LearningAdvanced Intelligent Systems10.1002/aisy.2023005636:3Online publication date: 26-Dec-2023
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