Resources of our paper "FilmAgent: A Multi-Agent Framework for End-to-End Film Automation in Virtual 3D Spaces". New versions in the making!

FilmAgent is a multi-agent collaborative system for end-to-end film automation in 3D virtual spaces. FilmAgent simulates key crew roles—directors, screenwriters, actors, and cinematographers, and integrates efficient human workflows within a sandbox environment.

Following the traditional film studio workflow, we divide the whole film automation process into three sequential stages: idea development, scriptwriting and cinematography, and apply the Critique-Correct-Verify, Debate-Judge collaboration strategies. After these stages, each line in the script is specified with the positions of the actors, their actions, their dialogue, and the chosen camera shots.

1. Install Package

conda create -n filmagent python==3.9.18
conda activate filmagent
pip install -r env.txt

2. Create Script and Logs folders in the Filmagent directory, then replace the absolute pathname '/path/to/' with your specific path and modify the topic in the main.py. Modify the api_key and organization number in LLMCaller.py. Run the following commands to get the movie script created by the agents collaboratively:

cd /path/to/FilmAgent
conda activate filmagent
python main.py

3. We use ChatTTS to provide voice acting for the characters in the script. You need to download the ChatTTS repository to the TTS directory. Then replace the absolute pathname '/path/to/' with your specific path in the tts_main.py. Run the following commands to deploy the text-to-speech service:

cd /path/to/TTS
conda create -n tts python==3.9.18
conda activate tts
pip install -r tts_env.txt
python tts_main.py

4. Modify the Script_path, actos_path, Audio_path and url in the GenerateAudio.py. Run the following commands to get the audio files:

cd /path/to/FilmAgent
conda activate filmagent
python GenerateAudio.py

5. We now have the script.json, actors_profile.json, and a series of .wav audio files. Next, we need to execute the script in Unity. The recommended version of the Unity editor is Unity 2022.3.14f1c1. You need to download the Unity project file we provide. After decompression, open TheBigBang\Assets\TheBigBang\Manyrooms.unity with Unity. Then replace all the absolute pathnames '/path/to/' with your specific path in TheBigBang\Assets\Scirpts\StartVideo.cs and TheBigBang\Assets\Scirpts\ScriptExecute.cs. Press 'ctrl+R' in the unity interface to recompile, click 'Play' to enter Game mode, then press 'E' to start executing the script (sometimes the audio files load slowly, so you may need to play it 2 or 3 times before it can run normally).

6. For the tests on 15 topics in our experimental section, we provide three .py files: test_full.py (The full FilmAgent framework, utilizing multi-agent collaboration.), test_no_interation.py (A single agent is responsible for planning, scriptwriting, and cinematography, representing our FilmAgent framework without multi-agent collaboration algorithms.) and test_cot.py (A single agent generates the chain-of-thought rationale and the complete script).

The following table records some comparisons of the scripts and camera settings before (left) and after (right) multi-agent collaboration, with excerpts from their discussion process.

Case #1 and #2 are from the Critique-Correct-Verify method in Scriptwriting #2 and #3 stages respectively. Case #3 and #4 are from the Debate-Judge method in Cinematography.

• Case #1 shows that Director-Screenwriter discussion reduces hallucinations in non-existent actions (e.g., standing suggest), enhances plot coherence, and ensures consistency across scenes.
• Case #2 shows that Actor-Director-Screenwriter discussion improves the alignment of dialogue with character profiles.
• Case #3, in the Debate-Judge method in cinematography, demonstrates the correction of an inappropriate dynamic shot, which is replaced with a medium shot to better convey body language.
• Case #4 replaces a series of identical static shots with a mix of dynamic and static shots, resulting in a more diverse camera setup.

While Sora shows great adaptability to diverse locations, characters and shots, it struggles with consistency and narrative delivery. In contrast, FilmAgent requires pre-built 3D spaces, but it produces coherent, physics-compliant videos with strong storytelling capabilities.

If you find FilmAgent useful for your research and applications, please cite using this BibTeX:

@misc{xu2025filmagent,
      title={FilmAgent: A Multi-Agent Framework for End-to-End Film Automation in Virtual 3D Spaces}, 
      author={Zhenran Xu and Longyue Wang and Jifang Wang and Zhouyi Li and Senbao Shi and Xue Yang and Yiyu Wang and Baotian Hu and Jun Yu and Min Zhang},
      year={2025},
      eprint={2501.12909},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.12909}, 
}