# Crowd Simulation

The authors propose Ctrl-CWM, a multi-agent controllable crowd world model that first learns and freezes an encoder via trajectory prediction on real pedestrian videos to preserve human motion dynamics, then uses an actor to propose displacements, a critic to rank imagined crowd trajectories, and a planner combining critic scores with user costs to select actions, enabling crowd generation and run-time control without retraining; on ETH–UCY, SDD, and GCS it outperforms CrowdES on most crowd realism and collision metrics and achieves the highest average compliance in avoidance and attraction scenarios.

The authors propose Ctrl-CWM, a multi-agent controllable crowd world model that first learns and freezes an encoder via trajectory prediction on real pedestrian videos to preserve human motion dynamics, then uses an actor to propose displacements, a critic to rank imagined crowd trajectories, and a planner combining critic scores with user costs to select actions, enabling crowd generation and run-time control without retraining; on ETH–UCY, SDD, and GCS it outperforms CrowdES on most crowd realism and collision metrics and achieves the highest average compliance in avoidance and attraction scenarios.