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
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ISBN | 9781510652163 |
ISSN | 0277-786X |
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 |
DOI | 10.1117/12.2631194 |
URL | 查看来源 |
语种 | 英语English |
Scopus入藏号 | 2-s2.0-85128334435 |
引用统计 | |
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. |
条目包含的文件 | 条目无相关文件。 |
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