“Learning a Perceptual Manifold With Deep Features for Animation Video Resequencing”, 2021-11-02 (; similar):
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 use 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 a 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 appealingly arrange unordered collections of images.