Semantic Image Synthesis of Anime Characters Based on Conditional Generative Adversarial Networks


Xuhui Zhu (Chongqing University), feng jiang (Chongqing University), Jing Wen (Chongqing University), yi wang (Chongqing University), qiang gao (Chongqing University)
The 35th British Machine Vision Conference
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Abstract

The goal of semantic image synthesis is to generate realistic images from semantic label maps. However, current approaches for generating anime characters from semantic label maps still encounter some issues, particularly the inability to directly generate a specific anime character from the semantic label map, as well as blurred colors and chaotic textures in the generated images. To address these issues, we propose a Conditional Generative Adversarial Network for Semantic Image Synthesis of Anime Characters. Specifically, in the generator, we propose character identity tensor to control the generation of specified anime characters, and introduce conditional noise to enable the generated images to have natural colors. Additionally, we redesign the discriminator as a network based on semantic segmentation and edge detection, which effectively supervises the texture details, guiding the generator to generate images with higher-quality textures. Experimental results show the superiority of our proposed method in generating specific and realistic anime characters compared to existing methods. Our source code is publicly available at https://github.com/hahahappyboy/Semantic-Image-Synthesis-of-Anime-Characters-Based-on-Conditional-Generative-Adversarial-Networks

Citation

@inproceedings{Zhu_2024_BMVC,
author    = {Xuhui Zhu and feng jiang and Jing Wen and yi wang and qiang gao},
title     = {Semantic Image Synthesis of Anime Characters Based on Conditional Generative Adversarial Networks},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0508.pdf}
}


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