NijiGAN: Transform What You See into Anime with Contrastive Semi-Supervised Learning and Neural Ordinary Differential Equations

• URL: http://arxiv.org/abs/2412.19455v1
• Date: Fri, 27 Dec 2024 04:46:44 GMT
• Title: NijiGAN: Transform What You See into Anime with Contrastive Semi-Supervised Learning and Neural Ordinary Differential Equations
• Authors: Kevin Putra Santoso, Anny Yuniarti, Dwiyasa Nakula, Dimas Prihady Setyawan, Adam Haidar Azizi, Jeany Aurellia P. Dewati, Farah Dhia Fadhila, Maria T. Elvara Bumbungan,
• Abstract summary: This research introduces NijiGAN, a novel model for image-to-image translation.<n>It transforms real-world scenes into high fidelity anime visuals using half of Scenimefy's parameters.<n>It employs pseudo-paired data generated through Scenimefy for supervised training, eliminating dependence on low-quality paired data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Generative AI has transformed the animation industry. Several models have been developed for image-to-image translation, particularly focusing on converting real-world images into anime through unpaired translation. Scenimefy, a notable approach utilizing contrastive learning, achieves high fidelity anime scene translation by addressing limited paired data through semi-supervised training. However, it faces limitations due to its reliance on paired data from a fine-tuned StyleGAN in the anime domain, often producing low-quality datasets. Additionally, Scenimefy's high parameter architecture presents opportunities for computational optimization. This research introduces NijiGAN, a novel model incorporating Neural Ordinary Differential Equations (NeuralODEs), which offer unique advantages in continuous transformation modeling compared to traditional residual networks. NijiGAN successfully transforms real-world scenes into high fidelity anime visuals using half of Scenimefy's parameters. It employs pseudo-paired data generated through Scenimefy for supervised training, eliminating dependence on low-quality paired data and improving the training process. Our comprehensive evaluation includes ablation studies, qualitative, and quantitative analysis comparing NijiGAN to similar models. The testing results demonstrate that NijiGAN produces higher-quality images compared to AnimeGAN, as evidenced by a Mean Opinion Score (MOS) of 2.192, it surpasses AnimeGAN's MOS of 2.160. Furthermore, our model achieved a Frechet Inception Distance (FID) score of 58.71, outperforming Scenimefy's FID score of 60.32. These results demonstrate that NijiGAN achieves competitive performance against existing state-of-the-arts, especially Scenimefy as the baseline model.

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