Dynamic Crowd Routing: RL-Driven Crowd Dynamics

The simulation of crowds is complex and challenging. Every individual in a crowd exhibits a different behaviour, targets a different goal, and undergoes different types of interactions. Within crowds, groups can be identified in both static and dynamic configurations, with varying levels of responsiveness, leading to the emergence of complex avoidance mechanisms. In the past, rule-based models have been proposed to simulate crowds, unlocking the potential for large-scale simulations. Over the years, learning-based solutions have been presented, achieving acceptable results despite the lack of high-quality ground truth data for training. While both rule-based and learning-based methods have recently been integrated into 3D simulation engines, they usually rely on navigation meshes or B-spline functions, hindering their generalization to open-world scenarios. In this work, we propose a reinforcement learning-based solution to learn meaningful crowd dynamics inside the Unreal Engine 3D engine, enabling massive and highly dynamic crowd simulations. We show how our proposed method makes it possible to simulate crowd setups that require complex dynamic routing mechanisms, which are otherwise hard to achieve using rule-based approaches or even deep learning-based methods. Our approach also al-lows us to easily collect large synthetic datasets that are both photorealistic and provide accurate ground truth data without the need for any manual annotation. Some demonstration videos are available at mmlab-cv.github.io/DynamicCrowdRouting; Code, complete experiments and analysis will be made available upon acceptance.