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Generative AI for Cel-Animation: A Survey

Created by
  • Haebom

Author

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, Xinyang Li, Zeliang Zhang, Chenliang Xu

Outline

Traditional cell animation production pipelines require extensive manual labor, technical expertise, and significant time investment, including storyboarding, layout design, keyframe animation, intermediate frame generation, and coloring, limiting efficiency and scalability. This paper explores how generative artificial intelligence (GenAI) can address these challenges by automating tasks such as intermediate frame generation, coloring, and storyboard creation. Integrating GenAI with tools like AniDoc, ToonCrafter, and AniSora lowers technical barriers, increasing accessibility to a wider range of creators, and freeing artists to focus on creative expression and artistic innovation. However, challenges remain, such as visual consistency, stylistic unity, and ethical considerations. We also discuss future directions and advancements in AI-assisted animation.

Takeaways, Limitations

Takeaways:
GenAI can be used to increase the efficiency and scalability of the cell animation production process.
Lowering technical barriers and enabling more artists to participate in animation production.
It helps artists focus more on their creative work.
It presents the potential of GenAI-based animation production tools such as AniDoc, ToonCrafter, and AniSora.
Limitations:
Difficulty maintaining visual consistency and stylistic unity.
Ethical considerations.
The need for continuous development and improvement of GenAI technology.
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