Reproduce “AniME: Adaptive Multi-Agent Planning for Long Animation Generation” on OpenResearch
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About this paper
Lisai Zhang, Baohan Xu, Siqian Yang, Mingyu Yin, Jing Liu, Chao Xu, Siqi Wang, Yidi Wu and 4 more
AniME presents a director-oriented multi-agent framework for automated, end-to-end long animation generation, orchestrating specialized generative AI models via an adaptive Model Context Protocol. The system produces cinematic-quality animation with consistent characters and synchronized audio-visual elements directly from a story script.
Key insights
- Complex long-form content generation benefits from a hierarchical, director-oriented multi-agent framework that orchestrates specialized tasks and ensures global coherence.
- An adaptive Model Context Protocol (MCP) enables specialized agents to intelligently select and apply generative tools, optimizing for task requirements and overcoming limitations of individual models.
- Maintaining a global asset memory bank and employing structured communication protocols are essential for preventing creative drift and ensuring consistency across diverse production stages and agents.
Summary by alphaXiv.
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