Animation image transfer using CycleGAN

Zhixun Liu; Yiheng Zhang; Xinyao Han; Wanting Zhou

doi:10.1117/12.2631194

18 March 2022 Animation image transfer using CycleGAN

Zhixun Liu, Yiheng Zhang, Xinyao Han, Wanting Zhou

Proceedings Volume 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021); 121680J (2022) https://doi.org/10.1117/12.2631194
Event: International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 2021, Harbin, China

Abstract

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.

Citation Download Citation

Zhixun Liu, Yiheng Zhang, Xinyao Han, and Wanting Zhou "Animation image transfer using CycleGAN", Proc. SPIE 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 121680J (18 March 2022); https://doi.org/10.1117/12.2631194

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Solid modeling

Image quality

Statistical modeling

Visualization

Convolution

Signal to noise ratio

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