Official Code of SocioMind for CVPR 2024 paper Digital Life Project: Autonomous 3D Characters with Social Intelligence.

conda create -n sociomind python=3.11
conda activate sociomind     
pip install -r requirements.txt

Download the required assets of SocioMind and extract them as:

-assets
    -sociomind
        -Arial.ttf
        -ipip_table.xlsx
        -persona_database.npy
        -spatial_map.json

If you want to generate the database of persona instructions, you can modify the file path of persona_database in config yamls in digital_life_project/characters/configs/.

If your API service is OpenAI, please follow the tutorials to set the OPENAI_API_KEY and OPENAI_BASE_URL in your environment.

If you use the OpenAI service from Azure, please set the AZURE_OPENAI_API_KEY and AZURE_OPENAI_ENDPOINT in the environment.

For other LLM options, please refer to digital_life_project/characters/llm_api/gpt_prompt_base.py for customization. Default LLM model for SocioMind is gpt-4o, and text model is text-embedding-ada-002.

In AI society simulations, we can run only the SocioMind component, which is an LLM-Agent structure based on psychological theories and is associated with the behaviors of 3D avatars in the text domain.

python sociomind_ai_society.py 
    --config qiqiang_anxin.yaml 
    --llm_service_type azure

• config: Config path for AI society simulation;
• llm_service: LLM service type, 'openai' or 'azure';
• replay_dir: You can continue your simulation from the output direction (one of the plot) of previous simulation.

Results are saved in the direction outout. You can check the txt and csv format recordings of the simulation. Besides, you can refer to each plot for visualization of emotion, keywords, and realationship.

Config settings are put in digital_life_project/characters/configs. Here we introduce some important parameters in the XXX.yaml files.

• mode: (Options: preconfigured, autonomous, event-driven. Default: autonomous). preconfigured: user can preset the behavior of the two characters in the config yaml. event-driven: user can manually set the events of each character in each plot. autonomous: plots can be generated autonomously.

• characters_info: A dict about the personality (Big Five Model), emotion (PAD model), motivation, core self, and relationships of each character. For quantitation of the proposed dimension, we use a Likert Scale: 1 ~ 9.

• plot_length: Maximal plots in this simulation.

• max_round_per_plot: Maximal rounds of interaction in each plot.

• plot_id: Plot id to start the simulation, espically for replay.

• events: Settings for event-driven mode. Events are in a list format. K-th item means the manual events at the K-th plot.

• predefined_plots: Settings for preconfigured mode.

We provide evaluation scripts in digital_life_project/characters/evaluation.py if you want to do ablation study.

@inproceedings{Cai2024DLP,
      title={Digital Life Project: Autonomous 3D Characters with Social Intelligence}, 
      author={Zhongang Cai, Jianping Jiang, Zhongfei Qing, Xinying Guo, Mingyuan Zhang, Zhengyu Lin, Haiyi Mei, Chen Wei, Ruisi Wang, Wanqi Yin, Xiangyu Fan, Han Du, Liang Pan, Peng Gao, Zhitao Yang, Yang Gao, Jiaqi Li, Tianxiang Ren, Yukun Wei, Xiaogang Wang, Chen Change Loy, Lei Yang, Ziwei Liu},
      booktitle={CVPR},
      year={2024}
}

Our code of SocioMind is based on Generative Agents.