Disney Research



Video synchronization is a fundamental step for many applications in computer vision, ranging from video morphing to motion analysis. We present a novel method for synchronizing action videos where a similar action is performed by different people at different times and different locations with different local speed changes, e.g., as in sports like weightlifting, baseball pitch, or dance. Our approach extends the popular “snapping” tool of video editing software and allows users to automatically snap action videos together in a timeline based on their content. Since the action can take place at di fferent locations, existing appearance-based methods are not appropriate. Our approach leverages motion information and computes a nonlinear synchronization of the input videos to establish frame-to-frame temporal correspondences. We demonstrate our approach can be applied for video synchronization, video annotation, and action snapshots. Our approach has been successfully evaluated with ground truth data and a user study.

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