Context-Aware Motion Diversification for Crowd Simulation

Traditional crowd simulation models typically focus on navigational pathfinding and local collision avoidance. Little research has explored how to optimally control individual agents´ detailed motions throughout a crowd. A proposed approach dynamically controls agents´ motion styles to increase a crowd´s motion variety. The central idea is to maximize both the style variety of local neighbors and global style utilization while maintaining a consistent style for each agent that´s as natural as possible. To assist runtime diversity control, an offline preprocessing algorithm extracts primitive motions from a motion capture database and stylizes them. This approach can complement most high-level crowd models to increase realistic variety. Four experiment scenarios and a user evaluation demonstrate this approach´s superior flexibility compared to traditional random distribution of motion styles. The Web extra is a video demonstrating a military-march simulation.