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home / papers / 2026 / MAViS: A Multi-Agent Framework for Long-Sequence Video Storytelling

MAViS: A Multi-Agent Framework for Long-Sequence Video Storytelling

Paper · 2026 · Qian Wang, Ziqi Huang, Ruoxi Jia et al.

Summary

Current generative video models can produce convincing single shots, but they struggle to build minute-long stories. Existing storytelling pipelines often need users to write scripts and train LoRA models by hand, and they still tend to produce distorted frames, repetitive motion and weak shot structure. MAViS addresses this with a multi-agent system that turns a short user prompt into a complete narrated video with background music. Specialised agents handle six consecutive stages: script writing, shot designing, character modeling (LoRA-based identity consistency), keyframe generation with T2I models, animation with I2V models, and audio generation. Within each stage, agents follow the 3E Principle. One agent drafts an output (Explore), a reviewer checks it against guidelines (Examine), and a refiner revises the request (Enhance). This loop repeats before the result moves to the next stage. The authors also propose Script Writing Guidelines, which steer scripts toward content that current generators can actually render. Experiments and user preference votes favour MAViS over prior systems on assistive capability, visual quality and expressiveness. Its modular design lets the underlying models be swapped.

Key points

  • The pipeline works end to end from a brief prompt. Each output is a sequence of N shots, and every shot pairs a video clip with an audio track. The authors claim MAViS is the only framework that delivers voice-over and background music together with the video.
  • The script stage uses three reviewers: Structure, Content and Style. Ablations suggest each one matters. Removing the Structure Reviewer hurts narrative expressiveness, removing the Content Reviewer lowers visual quality, and removing the Style Reviewer weakens alignment with user intent.
  • The Shot Reviewer, Voice Reviewer and Subtitle Refiner were evaluated with a Compliance Rate metric, defined as the share of outputs that fully meet their guidelines. Removing these agents lowered compliance, with the largest drop in voice-over generation.
  • In the T2I/I2V ablation, Dynamic Degree was higher without 3E (44.58 vs. 35.63). The authors attribute this to the agents preferring calmer, more physically stable clips.
  • Stated limitations:
    • The method is restricted to a T2I+I2V paradigm.
    • Stories contain no character dialogue and the framework has no video editing.
    • The multimodal evaluators are unreliable. In practice the authors add VBench operators to support them, but they excluded these from the experiments for fairness.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Nanyang Technological UniversitySingapore · Singapore156 works · 25,644 citations🎓Virginia TechBlacksburg · United States36 works · 43,036 citations

 Fields, topics and concepts

📚 Shelves 3The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksComputer Vision and Pattern Recognition · 2,869 worksComputer Graphics and Computer-Aided Design · 7 works

Cite

Cite as Qian Wang, Ziqi Huang, Ruoxi Jia, Paul Debevec and Ning Yu. 2026. MAViS: A Multi-Agent Framework for Long-Sequence Video Storytelling. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.eacl-long.101

@inproceedings{wang2026mavis,
  title = {{MAViS: A Multi-Agent Framework for Long-Sequence Video Storytelling}},
  author = {Qian Wang, Ziqi Huang, Ruoxi Jia, Paul Debevec and Ning Yu},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.101},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.101}
}

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