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Virtual Community: An Open World for
Humans, Robots, and Society

arXiv 2025

Qinhong Zhou*, Hongxin Zhang*, Xiangye Lin*, Zheyuan Zhang*, Yutian Chen, Wenjun Liu, Zunzhe Zhang, Sunli Chen, Lixing Fang, Qiushi Lyu, Xinyu Sun, Jincheng Yang, Zeyuan Wang, Bao Chi Dang, Zhehuan Chen, Daksha Ladia, Quang Vinh Dang, Jiageng Liu, Chuang Gan

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Virtual Community is an open-world platform that simulates human-robot coexistence in shared communities, featuring a physics-based multi-agent simulator and real-world 3D scenes to study embodied social intelligence at scale.

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Tabel of Contents
  1. News
  2. Getting Started
  3. Acknowledgement
  4. Citation

News

  • [2025-06-19] We have released the Virtual Community! Check out the project website and our paper for more details.

Getting Started

Installation

We're using python 3.11, cuda 11.7 and ubuntu 24.04. If you are using a different version, please modify the env.yaml file accordingly.

conda env create -f env.yaml

Install Genesis from the source:

cd Genesis
pip install -e .

To run the example tour agent, install volume grid lib:

cd agents/sg
./setup.sh

Assets Preparation

Download the assets from the Google Drive and organize them under Genesis/genesis/assets/ViCo.

Assets include:

  • scene folder: contains the generated outdoor 3D scenes.
  • robots folder: contains the robot models.
  • objects folder: contains the object models.
  • avatars folder: contains the human avatars' skin and motion.
  • cars folder: contains the vehicle models including car, bus, bike, etc.

If you want to use indoor scenes from GRUTopia (otherwise use --no_load_indoor_scene flag), please follow their instructions here to download the commercial_scenes.zip, and unzip it under Genesis/genesis/assets/ViCo/scene/.

After this step, the directory structure should be:

Genesis/genesis/assets/ViCo/
├── scene/
├──── v1/
├──── commercial_scenes/ (optional)
├── robots/
├── objects/
├── avatars/
└── cars/

Run the Simulation

Run the following script to test the simulation, where 15 agents will tour around the New York Scene.

./scripts/run_tour_agent.sh

Tips: Loading all indoor scenes and objects may take around 40G memory. If you encounter memory issues, consider adding --no_load_indoor_scene flag to the script to disable indoor scenes.

System Requirements

System Memory (RAM)

  • **Minimum**: 24 GB
  • **Recommended**: 32 GB

GPU Memory (VRAM)

  • **Minimum**: 10 GB
  • **Recommended**: 16 GB

Disk Space

  • **Minimum**: 60 GB
  • **Recommended**: 100 GB

TroubleShooting

If you encounter any issues, please check the following:

libstdcxx. so.6: version GLIBCXX 3.4.32’not found required by sg/builder/builtin/libbuider.so)

To resolve this issue, you may need to update your GCC runtime. You can do this by running:

conda install -c conda-forge libstdcxx-ng

Only pygltflib-1.16.0 works for now, the latest version pygltflib-1.16.1 will slower the scene loading.

Acknowledgement

Virtual Community is built on lots of amazing open-source projects:

  • Genesis: Universal physics engine backend. Thanks for the team for their great work on Genesis, which provides the foundation for our project.
  • GRUtopia: The indoor scene dataset we used for retrieval.
  • ARCHITECT: The indoor scene generation method we used.
  • Google 3D Tiles: The source of our outdoor scene data.
  • OpenStreetMap: The source of our outdoor scene data.
  • Blender: The 3D modeling software we used to create and modify the assets.
  • ...

And inspiring works that influenced our design and implementation:

  • Generative Agents: Inspiring our character generation.
  • V-IRL: Inspiring our leverage of real geospatial data.
  • ...

With many more not listed here, we are grateful for the contributions of the open-source community that made this project possible.

Citation

If you find this work useful, please consider citing:

@misc{zhou2025virtualcommunityopenworld,
      title={Virtual Community: An Open World for Humans, Robots, and Society}, 
      author={Qinhong Zhou and Hongxin Zhang and Xiangye Lin and Zheyuan Zhang and Yutian Chen and Wenjun Liu and Zunzhe Zhang and Sunli Chen and Lixing Fang and Qiushi Lyu and Xinyu Sun and Jincheng Yang and Zeyuan Wang and Bao Chi Dang and Zhehuan Chen and Daksha Ladia and Jiageng Liu and Chuang Gan},
      year={2025},
      eprint={2508.14893},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2508.14893}, 
}

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