Learning a Perceptual Manifold with Deep Features for Animation Video Resequencing

Charles C. Morace Thi-Ngoc-Hanh Le Sheng-Yi Yao Shang-Wei Zhang Tong-Yee Lee

Department of Computer Science and Information Engineering
National Cheng Kung University

[Main Video] [Additional Results] [Appendix]


This paper is under second round review. Early version is avalable on arXiv.

Submitted: January 2021 / Revised: Major revision in June 2021





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, 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 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 appealingly arrange unordered collections of images.