FilmAgent: A Multi-Agent Framework for End-to-End Film Automation in
  Virtual 3D Spaces
Authors: name: Zhenran Xu, name: Longyue Wang, name: Jifang Wang, name: Zhouyi Li, name: Senbao Shi, name: Xue Yang, name: Yiyu Wang, name: Baotian Hu, name: Jun Yu, name: Min Zhang

Virtual film production requires intricate decision-making processes,
including scriptwriting, virtual cinematography, and precise actor positioning
and actions. Motivated by recent advances in automated decision-making with
language agent-based societies, this paper introduces FilmAgent, a novel
LLM-based multi-agent collaborative framework for end-to-end film automation in
our constructed 3D virtual spaces. FilmAgent simulates various crew roles,
including directors, screenwriters, actors, and cinematographers, and covers
key stages of a film production workflow: (1) idea development transforms
brainstormed ideas into structured story outlines; (2) scriptwriting elaborates
on dialogue and character actions for each scene; (3) cinematography determines
the camera setups for each shot. A team of agents collaborates through
iterative feedback and revisions, thereby verifying intermediate scripts and
reducing hallucinations. We evaluate the generated videos on 15 ideas and 4 key
aspects. Human evaluation shows that FilmAgent outperforms all baselines across
all aspects and scores 3.98 out of 5 on average, showing the feasibility of
multi-agent collaboration in filmmaking. Further analysis reveals that
FilmAgent, despite using the less advanced GPT-4o model, surpasses the
single-agent o1, showing the advantage of a well-coordinated multi-agent
system. Lastly, we discuss the complementary strengths and weaknesses of
OpenAI's text-to-video model Sora and our FilmAgent in filmmaking.