Data-Driven Crowd Simulation with Generative Adversarial Networks
Javad Amirian, Wouter van Toll, Jean-Bernard Hayet, Julien Pettré
In 32nd International Conference on Computer Animation and Social Agents, pp. 7-10, 2019.
Resources:
ACM Digital Library page - Inria HAL page - Full paper (PDF)
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.
@inproceedings{Amirian2019-CrowdGAN,
author = {Amirian, J. and van Toll, W. and Hayet, J.-B. and Pettr\'e, J.},
title = {Data-Driven Crowd Simulation with Generative Adversarial Networks},
booktitle = {Proceedings of the 32nd International Conference on Computer Animation and Social Agents},
year = {2019},
pages = {7--10},
}