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

研究成果: Article同行評審

摘要

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 appealingly arrange unordered collections of images.

原文English
頁(從 - 到)23687-23707
頁數21
期刊Multimedia Tools and Applications
81
發行號17
DOIs
出版狀態Published - 2022 7月

All Science Journal Classification (ASJC) codes

  • 軟體
  • 媒體技術
  • 硬體和架構
  • 電腦網路與通信

指紋

深入研究「Learning a perceptual manifold with deep features for animation video resequencing」主題。共同形成了獨特的指紋。

引用此