Generative AI for Cel-Animation: A Survey

2025-01-08

AI Summary

Traditional cel animation production involves labor-intensive, technically demanding workflows that severely hinder creative efficiency and broad adoption. Method: This paper systematically surveys generative AI applications across the entire cel animation pipeline—including storyboard generation, keyframe assistance, in-between interpolation, and coloring—and introduces the first comprehensive technical taxonomy for GenAI-powered cel animation. We formulate core challenges centered on cross-stage consistency and controllable stylistic fidelity. Our approach integrates large language models (LLMs), multimodal understanding models, and diffusion models to enable semantic generation from text or sketch inputs and pixel-level stylized rendering. We also survey open-source tools (e.g., AniDoc, ToonCrafter) and release AI4Animation—the first dedicated open-source resource repository for AI-driven animation (hosted on GitHub). Contribution/Results: The framework significantly reduces manual effort, empowering small studios and independent animators, thereby advancing cost reduction, efficiency gains, and democratization of animation creation.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Natural Language Processing: Generation
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Agentic search

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

Problem

• Traditional Animation Production
• Complex Process
• Time-consuming

Innovation

• Artificial Intelligence
• Animation Production
• Accessibility Enhancement