Explicit Modeling of Personal Space for Improved Local Dynamics in Simulated Crowds
提出一种基于区域的惩罚力方法显式建模个人空间，无需昂贵的最近邻搜索，可在交互帧率下模拟数千实体，适用于严肃游戏和娱乐领域的人群仿真。
Crowd simulation demands careful consideration in regard to the classic trade-off between accuracy and efficiency. Particle-based methods have seen success in various applications in architecture, military, urban planning, and entertainment. This method focuses on local dynamics of individuals in large crowds, with a focus on serious games and entertainment. The technique uses an area-based penalty force that captures the infringement of each entity's personal space. This method does not need a costly nearest-neighbor search and allows for an inherently data-parallel implementation capable of simulating thousands of entities at interactive frame rates. The algorithm reproduces personal space compression around motion barriers for moving crowds and around points of interest for static crowds.