Automated Movie Generation via Multi-Agent CoT Planning
Authors: name: Weijia Wu, name: Zeyu Zhu, name: Mike Zheng Shou

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.