{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/semantic-image-synthesis-of-anime-characters","title":"semantic image synthesis of anime characters based on conditional generative adversarial networks","arxiv_id":null,"date":"2024-11-25","proceeding":"British Machine Vision Conference 2024 11","authors":["Xuhui Zhu; Feng Jiang; Jing Wen; Yi Wang; Qiang Gao"],"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.","url_abs":"https://bmvc2024.org/proceedings/508/","url_pdf":"https://bmvc2024.org/proceedings/508/","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"semantic-image-synthesis-of-anime-characters","repo_url":"https://github.com/hahahappyboy/Semantic-Image-Synthesis-of-Anime-Characters-Based-on-Conditional-Generative-Adversarial-Networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"edge-detection","task_name":"Edge Detection"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}