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Generative AI for Cel-Animation: A Survey

8 January 2025
Yunlong Tang
Junjia Guo
Pinxin Liu
Zhiyuan Wang
Hang Hua
Jia-Xing Zhong
Yunzhong Xiao
Chao Huang
Luchuan Song
Susan Liang
Yizhi Song
Liu He
Jing Bi
Mingqian Feng
X. Li
Zeliang Zhang
Chenliang Xu
    VGen
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Abstract

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, issues such as maintaining visual consistency, ensuring stylistic coherence, and addressing ethical considerations continue to pose challenges. Furthermore, this paper discusses future directions and explores potential advancements in AI-assisted animation. For further exploration and resources, please visit our GitHub repository:this https URL

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