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Paper · arXiv 2604.24842

Co-Director: Agentic Generative Video Storytelling

Yale Song, Yiwen Song, Nick Losier, Nathan Hodson, Ye Jin, Rhyard Zhu, Yan Xu, Daniel Vlasic, Carina Claassen, Jasmine Leon, Khanh G. LeViet, Zack Chomyn, Joe Timmons, Brett Slatkin, Scott Penberthy, Tomas Pfister

17 upvotesApril 27, 2026arXiv 预印本
AI 摘要

Co-Director presents a hierarchical multi-agent framework that formulates video storytelling as a global optimization problem, using multi-armed bandits and multimodal self-refinement to maintain semantic coherence and outperform existing approaches.

diffusion modelsagentic pipelinessemantic driftcascading failureshierarchical multi-agent frameworkglobal optimization problemmulti-armed banditmultimodal self-refinementsemantic coherencevideo storytelling

Abstract

While diffusion models generate high-fidelity video clips, transforming them into coherent storytelling engines remains challenging. Current agentic pipelines automate this via chained modules but suffer from semantic drift and cascading failures due to independent, handcrafted prompting. We present Co-Director, a hierarchical multi-agent framework formalizing video storytelling as a global optimization problem. To ensure semantic coherence, we introduce hierarchical parameterization: a multi-armed bandit globally identifies promising creative directions, while a local multimodal self-refinement loop mitigates identity drift and ensures sequence-level consistency. This balances the exploration of novel narrative strategies with the exploitation of effective creative configurations. For evaluation, we introduce GenAD-Bench, a 400-scenario dataset of fictional products for personalized advertising. Experiments demonstrate that Co-Director significantly outperforms state-of-the-art baselines, offering a principled approach that seamlessly generalizes to broader cinematic narratives. Project Page: https://co-director-agent.github.io/

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