Generative AI for Cel-Animation: A Survey
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, 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
Cite
@article{arxiv.2501.06250,
title = {Generative AI for Cel-Animation: A Survey},
author = {Yolo Y. Tang and Junjia Guo and Pinxin Liu and Zhiyuan Wang and Hang Hua and Jia-Xing Zhong and Yunzhong Xiao and Chao Huang and Luchuan Song and Susan Liang and Yizhi Song and Liu He and Jing Bi and Mingqian Feng and Xinyang Li and Zeliang Zhang and Chenliang Xu},
journal= {arXiv preprint arXiv:2501.06250},
year = {2025}
}
Comments
Accepted by ICCV 2025 AISTORY Workshop