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Data-Driven Macroscopic Crowd Animation Synthesis Method using Velocity Fields

机译:基于速度场的数据驱动宏观人群动画合成方法

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This paper address the problem of synthesizing realistic crowd behavior animation in virtual environment, which usually requires setting a lot of empirical parameters and interacting with the users frequently. A new method is proposed in this paper, which extracts the typical pedestrian velocity fields from video data, and then synthesizes the macroscopic crowd behavior animation based on these fields. The video data used to extract velocity fields was generated from a specifically designed experiment. With these velocity fields, we could design the macroscopic crowd behavior interactively in a 2D behavior editor, and synthesized the corresponding real time 3D crowd animation. Experiments results have proved that this new method can get a good performance of realistic crowd animation synthesis with little user interaction, and can be applied in many ways.
机译:本文提出了在虚拟环境中合成逼真的人群行为动画的问题,这通常需要设置大量的经验参数并经常与用户进行交互。本文提出了一种新的方法,该方法从视频数据中提取典型的行人速度场,然后基于这些场合成宏观的人群行为动画。用于提取速度场的视频数据是从专门设计的实验中生成的。利用这些速度场,我们可以在2D行为编辑器中交互式设计宏观人群行为,并合成相应的实时3D人群动画。实验结果证明,该新方法在用户交互较少的情况下,可以获得较好的逼真的人群动画合成性能,并且可以以多种方式应用。

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