  We present a framework for video-driven crowd synthesis. Motion vectors
extracted from input crowd video are processed to compute global motion paths.
These paths encode the dominant motions observed in the input video. These
paths are then fed into a behavior-based crowd simulation framework, which is
responsible for synthesizing crowd animations that respect the motion patterns
observed in the video. Our system synthesizes 3D virtual crowds by animating
virtual humans along the trajectories returned by the crowd simulation
framework. We also propose a new metric for comparing the "visual similarity"
between the synthesized crowd and exemplar crowd. We demonstrate the proposed
approach on crowd videos collected under different settings.
