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We propose a novel deep learning framework for animation video resequencing.Our system produces new video sequences by minimizing a perceptual distance ofimages from an existing animation video clip. To measure perceptual distance,we utilize the activations of convolutional neural networks and learn aperceptual distance by training these features on a small network with datacomprised of human perceptual judgments. We show that with this perceptualmetric and graph-based manifold learning techniques, our framework can producenew smooth and visually appealing animation video results for a variety ofanimation video styles. In contrast to previous work on animation videoresequencing, the proposed framework applies to wide range of image styles anddoes not require hand-crafted feature extraction, background subtraction, orfeature correspondence. In addition, we also show that our framework hasapplications to appealing arrange unordered collections of images.

Learning a perceptual manifold with deep features for animation video resequencing