Co-Director: Agentic Generative Video Storytelling
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917442268168192 |
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| author | Song, Yale Song, Yiwen Losier, Nick Hodson, Nathan Jin, Ye Zhu, Rhyard Xu, Yan Vlasic, Daniel Claassen, Carina Leon, Jasmine LeViet, Khanh G. Chomyn, Zack Timmons, Joe Slatkin, Brett Penberthy, Scott Pfister, Tomas |
| author_facet | Song, Yale Song, Yiwen Losier, Nick Hodson, Nathan Jin, Ye Zhu, Rhyard Xu, Yan Vlasic, Daniel Claassen, Carina Leon, Jasmine LeViet, Khanh G. Chomyn, Zack Timmons, Joe Slatkin, Brett Penberthy, Scott Pfister, Tomas |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24842 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Co-Director: Agentic Generative Video Storytelling Song, Yale Song, Yiwen Losier, Nick Hodson, Nathan Jin, Ye Zhu, Rhyard Xu, Yan Vlasic, Daniel Claassen, Carina Leon, Jasmine LeViet, Khanh G. Chomyn, Zack Timmons, Joe Slatkin, Brett Penberthy, Scott Pfister, Tomas Artificial Intelligence Multiagent Systems Multimedia 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/ |
| title | Co-Director: Agentic Generative Video Storytelling |
| topic | Artificial Intelligence Multiagent Systems Multimedia |
| url | https://arxiv.org/abs/2604.24842 |