Context-Aware Motion Diversification for Crowd Simulation

  • Authors:
  • Qin Gu;Zhigang Deng

  • Affiliations:
  • University of Houston;University of Houston

  • Venue:
  • IEEE Computer Graphics and Applications
  • Year:
  • 2011

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Abstract

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.