  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.
