Smooth and efficient crowd transformation

Mingliang Xu, Yunpeng Wu, Yangdong Ye

Abstract: Crowd transformation has been an important research field due to its diverse range of applications that include film production, computer games, robotics and performance training. We propose a novel approach with continuous space and continuous time for smooth crowd transformation. Most algorithms simulating the transformation of crowds focus on the trajectories of individual participants. As a contrast, the approach we proposed here focuses on the balanced assignment to maintain the collective behavior of the full crowd. We use a bitmap-based recognition of the starting and final formations and quantify the performance of the transformation with the mutual information. We demonstrate that our method is computationally efficient for several group sizes and formations.