Deep learning for anime style transfer

Der Lor Way, Wei Cheng Chang, Zen Chung Shih

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

Some artificial systems based on a deep neural network create artistic images of high perceptual quality. However, it is usually suitable for use in abstract styles. The performances of existing style transfer algorithms on anime style are not very satisfactory, because it is either not sufficiently stylized or distorted severely in comic characters' domain. In this paper, we propose a novel anime style transfer algorithm using deep neural network, which treats foreground and background differently. Moreover, our method also could transfer the style for video with a style image. We combine semantic segmentation and spatial control to transfer the specified style to the specified area. By designing the initial image and the loss function. Users could adjust the feature weights of different regions to maintain the artistic conception of the target style, and combine optical flow to ensure frame coherence in a video. Finally, some experimental results demonstrate the effectiveness of our proposed method.

原文English
主出版物標題ICAIP 2019 - 2019 3rd International Conference on Advances in Image Processing
發行者Association for Computing Machinery
頁面139-143
頁數5
ISBN(電子)9781450376754
DOIs
出版狀態Published - 3 11月 2019
事件3rd International Conference on Advances in Image Processing, ICAIP 2019 - Chengdu, China
持續時間: 8 11月 201910 11月 2019

出版系列

名字ACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Advances in Image Processing, ICAIP 2019
國家/地區China
城市Chengdu
期間8/11/1910/11/19

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