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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.06250 |
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| _version_ | 1866918216750596096 |
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| author | Tang, Yolo Y. Guo, Junjia Liu, Pinxin Wang, Zhiyuan Hua, Hang Zhong, Jia-Xing Xiao, Yunzhong Huang, Chao Song, Luchuan Liang, Susan Song, Yizhi He, Liu Bi, Jing Feng, Mingqian Li, Xinyang Zhang, Zeliang Xu, Chenliang |
| author_facet | Tang, Yolo Y. Guo, Junjia Liu, Pinxin Wang, Zhiyuan Hua, Hang Zhong, Jia-Xing Xiao, Yunzhong Huang, Chao Song, Luchuan Liang, Susan Song, Yizhi He, Liu Bi, Jing Feng, Mingqian Li, Xinyang Zhang, Zeliang Xu, Chenliang |
| contents | Traditional Celluloid (Cel) Animation production pipeline encompasses multiple essential steps, including storyboarding, layout design, keyframe animation, inbetweening, and colorization, which demand substantial manual effort, technical expertise, and significant time investment. These challenges have historically impeded the efficiency and scalability of Cel-Animation production. The rise of generative artificial intelligence (GenAI), encompassing large language models, multimodal models, and diffusion models, offers innovative solutions by automating tasks such as inbetween frame generation, colorization, and storyboard creation. This survey explores how GenAI integration is revolutionizing traditional animation workflows by lowering technical barriers, broadening accessibility for a wider range of creators through tools like AniDoc, ToonCrafter, and AniSora, and enabling artists to focus more on creative expression and artistic innovation. Despite its potential, challenges like visual consistency, stylistic coherence, and ethical considerations persist. Additionally, this paper explores future directions and advancements in AI-assisted animation. For further exploration and resources, please visit our GitHub repository: https://github.com/yunlong10/Awesome-AI4Animation |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06250 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative AI for Cel-Animation: A Survey Tang, Yolo Y. Guo, Junjia Liu, Pinxin Wang, Zhiyuan Hua, Hang Zhong, Jia-Xing Xiao, Yunzhong Huang, Chao Song, Luchuan Liang, Susan Song, Yizhi He, Liu Bi, Jing Feng, Mingqian Li, Xinyang Zhang, Zeliang Xu, Chenliang Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Traditional Celluloid (Cel) Animation production pipeline encompasses multiple essential steps, including storyboarding, layout design, keyframe animation, inbetweening, and colorization, which demand substantial manual effort, technical expertise, and significant time investment. These challenges have historically impeded the efficiency and scalability of Cel-Animation production. The rise of generative artificial intelligence (GenAI), encompassing large language models, multimodal models, and diffusion models, offers innovative solutions by automating tasks such as inbetween frame generation, colorization, and storyboard creation. This survey explores how GenAI integration is revolutionizing traditional animation workflows by lowering technical barriers, broadening accessibility for a wider range of creators through tools like AniDoc, ToonCrafter, and AniSora, and enabling artists to focus more on creative expression and artistic innovation. Despite its potential, challenges like visual consistency, stylistic coherence, and ethical considerations persist. Additionally, this paper explores future directions and advancements in AI-assisted animation. For further exploration and resources, please visit our GitHub repository: https://github.com/yunlong10/Awesome-AI4Animation |
| title | Generative AI for Cel-Animation: A Survey |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2501.06250 |