科研成果详情

题名Animation Image Transfer using CycleGAN
作者
发表日期2022
会议名称2021 International Conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2021
会议录名称Proceedings of SPIE - The International Society for Optical Engineering
ISBN9781510652163
ISSN0277-786X
卷号12168
会议日期December 24-26, 2021
会议地点Harbin
摘要

Deep learning based generative models can generate pictures with desirable fidelity and quality. In this paper, we implemented CycleGAN that doesn't rely on paired datasets in animation industry to transform natural landscape pictures into Japanese animation style pictures. As demonstrated by a set of comprehensive benchmarks, we assume CycleGAN may have the potential to upend the whole animation industry. Numerous results on our dataset show the effectiveness of the proposed method. Our method finally obtains 0.9877 of PSNR and 17.1522 of SSIM, and we also visualize the output results of our images. Our method can give a brief attempt of image style transfer, which may be widely applied to many other different areas.

关键词Animation image Computer vision CycleGAN Deep learning Style transfer
DOI10.1117/12.2631194
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语种英语English
Scopus入藏号2-s2.0-85128334435
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被引频次[WOS]:0   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/8923
专题北师香港浸会大学
通讯作者Han, Xinyao
作者单位
1.Bell Honors School,Nanjing University of Posts and Telecommunications,Nanjing,Jiangsu,210023,China
2.Division of Science and Technology,United International College,Zhuhai,Guangdong,37008,China
3.Computer Science,Huazhong University of Science and Technology,Wuhan,Hubei,110100,China
通讯作者单位北师香港浸会大学
推荐引用方式
GB/T 7714
Liu, Zhixun,Zhang, Yiheng,Han, Xinyaoet al. Animation Image Transfer using CycleGAN[C], 2022.
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