Unpaired Cartoon Image Synthesis via Gated Cycle Mapping

Men, Yifang; Yao, Yuan; Cui, Miaomiao; Lian, Zhouhui; Xie, Xuansong; Hua, Xian-Sheng

doi:10.1109/cvpr52688.2022.00349

Cited by 13 publications

(1 citation statement)

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“…Zhuang and Yang [14] employ a few-shot technique for image-to-image translation in generating cartoon faces. Men et al [21] utilize an unpaired image synthesis method to transform human portraits into anime faces. Tang et al [6] explore unpaired image-to-image translation using attention GAN.…”

Section: Related Workmentioning

confidence: 99%

2D virtual YouTuber character generation using generative adversarial networks

Thedwichienchai,

Siriborvornratanakul

2024

Fifth International Conference on Computer Vision and Computational Intelligence (CVCI 2024)

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Virtual YouTubers (VTubers) offer significant growth potential as a new type of content creator. However, the financial aspect poses a hurdle for aspiring VTubers. This article proposes a cost-effective solution by utilizing generative models to create full-body images of 2D VTuber characters. Notably, studies have achieved remarkable results using Generative Adversarial Networks (GANs), including Deep Convolutional GAN (DCGAN) and Style-based GAN 2 (StyleGAN2), for anime face generation. To address the lack of image synthesis systems for full-body anime characters, experiments were conducted with DCGAN and StyleGAN2 on the Danbooru dataset. The results demonstrate that StyleGAN2 models outperform DCGAN, yielding superior Fréchet Inception Distance (FID) scores of 25.06, 28.03, and 24.52, compared to DCGAN's 159.21 FID score. This research contributes to reducing the cost associated with becoming a VTuber and offers insights into generating 2D full-body anime characters for VTubers.

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Section: Related Workmentioning

confidence: 99%