Automated Movie Generation via Multi-Agent CoT Planning
Existing long-form video generation frameworks lack automated planning,
requiring manual input for storylines, scenes, cinematography, and character
interactions, resulting in high costs and inefficiencies. To address these
challenges, we present MovieAgent, an automated movie generation via
multi-agent Chain of Thought (CoT) planning. MovieAgent offers two key
advantages: 1) We firstly explore and define the paradigm of automated
movie/long-video generation. Given a script and character bank, our MovieAgent
can generates multi-scene, multi-shot long-form videos with a coherent
narrative, while ensuring character consistency, synchronized subtitles, and
stable audio throughout the film. 2) MovieAgent introduces a hierarchical
CoT-based reasoning process to automatically structure scenes, camera settings,
and cinematography, significantly reducing human effort. By employing multiple
LLM agents to simulate the roles of a director, screenwriter, storyboard
artist, and location manager, MovieAgent streamlines the production pipeline.
Experiments demonstrate that MovieAgent achieves new state-of-the-art results
in script faithfulness, character consistency, and narrative coherence. Our
hierarchical framework takes a step forward and provides new insights into
fully automated movie generation. The code and project website are available
at: https://github.com/showlab/MovieAgent and
https://weijiawu.github.io/MovieAgent.
67cfa750c8f2a661dc9798fe
Weijia Wu
admin_assigned
2025-03-11T11:00:27.031Z
6345a93afe134dfd7a0cfabd
/avatars/65130ce06b1c72ab1066678419731d88.svg
wu weijia
user
weijiawu
67cfa750c8f2a661dc9798ff
Zeyu Zhu
67cfa750c8f2a661dc979900
Mike Zheng Shou
admin_assigned
2025-03-11T11:00:52.927Z
661ab3da2b14565c7acccf5c
/avatars/fa4fc03664803e02aede4d4c3d50b393.svg
Mike Zheng Shou
user
AnalMom
Automated Movie Generation via Multi-Agent CoT Planning
Existing long-form video generation frameworks lack automated planning,
requiring manual input for storylines, scenes, cinematography, and character
interactions, resulting in high costs and inefficiencies. To address these
challenges, we present MovieAgent, an automated movie generation via
multi-agent Chain of Thought (CoT) planning. MovieAgent offers two key
advantages: 1) We firstly explore and define the paradigm of automated
movie/long-video generation. Given a script and character bank, our MovieAgent
can generates multi-scene, multi-shot long-form videos with a coherent
narrative, while ensuring character consistency, synchronized subtitles, and
stable audio throughout the film. 2) MovieAgent introduces a hierarchical
CoT-based reasoning process to automatically structure scenes, camera settings,
and cinematography, significantly reducing human effort. By employing multiple
LLM agents to simulate the roles of a director, screenwriter, storyboard
artist, and location manager, MovieAgent streamlines the production pipeline.
Experiments demonstrate that MovieAgent achieves new state-of-the-art results
in script faithfulness, character consistency, and narrative coherence. Our
hierarchical framework takes a step forward and provides new insights into
fully automated movie generation. The code and project website are available
at: https://github.com/showlab/MovieAgent and
https://weijiawu.github.io/MovieAgent.
MovieAgent automates movie generation through a hierarchical Chain of Thought framework using multiple language models to handle narrative structuring, ensuring script fidelity, character consistency, and narrative coherence.
Chain of Thought (CoT) multi-agent system automated movie generation hierarchical reasoning language models director screenwriter storyboard artist location manager script faithfulness character consistency narrative coherence