Generative AI for Cel-Animation: A Survey
Authors: name: Yunlong Tang, name: Junjia Guo, name: Pinxin Liu, name: Zhiyuan Wang, name: Hang Hua, name: Jia-Xing Zhong, name: Yunzhong Xiao, name: Chao Huang, name: Luchuan Song, name: Susan Liang, name: Yizhi Song, name: Liu He, name: Jing Bi, name: Mingqian Feng, name: Xinyang Li, name: Zeliang Zhang, name: Chenliang Xu

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:
https://github.com/yunlong10/Awesome-AI4Animation