Learning a perceptual manifold with deep features for animation video resequencing

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