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CVPR 2024: Language Guided Generation of 3D Embodied AI Environments.
| Date | Stars |
|---|---|
| 2026-07-24 | 562 |
| 2026-07-25 | 562 |
| 2026-07-28 | 562 |
| 2026-07-30 | 562 |
| 2026-08-06 | 562 |
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<h2 align="center">
<img src="https://yueyang1996.github.io/images/logo.png" width="200px"/><br/>
Language Guided Generation of 3D Embodied AI Environments<br>
</h2>
<h5 align="center">
<img src="https://yueyang1996.github.io/images/office_example.png" width="800px"/><br/>
</h5>
<h4 align="center">
<a href="https://arxiv.org/abs/2312.09067"><i>Paper</i></a> | <a href="https://yueyang1996.github.io/holodeck/"><i>Project Page</i></a>
</h4>
## Requirements
Holodeck is based on [AI2-THOR](https://ai2thor.allenai.org/ithor/documentation/#requirements), and we currently support macOS 10.9+ or Ubuntu 14.04+.
**New Feature**: To add ANY new assets to AI2-THOR, please check the [objathor repo](https://github.com/allenai/objathor)!
**Note:** To yield better layouts, use `DFS` as the solver. If you pull the repo before `12/28/2023`, you must set the [argument](https://github.com/allenai/Holodeck/blob/386b0a868def29175436dc3b1ed85b6309eb3cad/main.py#L78) `--use_milp` to `False` to use `DFS`.
## Installation
After cloning the repo, you can install the required dependencies using the following commands:
```
conda create --name holodeck python=3.10
conda activate holodeck
pip install -r requirements.txt
pip install --extra-index-url https://ai2thor-pypi.allenai.org ai2thor==0+8524eadda94df0ab2dbb2ef5a577e4d37c712897
```
## Data
Download the data by running the following commands:
```bash
python -m objathor.dataset.download_holodeck_base_data --version 2023_09_23
python -m objathor.dataset.download_assets --version 2023_09_23
python -m objathor.dataset.download_annotations --version 2023_09_23
python -m objathor.dataset.download_features --version 2023_09_23
```
by default these will save to `~/.objathor-assets/...`, you can change this director by specifying the `--path` argument. If you change the `--path`, you'll need to set the `OBJAVERSE_ASSETS_DIR` environment variable to the path where the assets are stored when you use Holodeck.
## Usage
You can use the following command to generate a new environment.
```
python holodeck/main.py --query "a living room" --openai_api_key <OPENAI_API_KEY>
```
Our system uses `gpt-4o-2024-05-13`, **so please ensure you have access to it.**
**Note:** To yield better layouts, use `DFS` as the solver. If you pull the repo before `12/28/2023`, you must set the [argument](https://github.com/allenai/Holodeck/blob/386b0a868def29175436dc3b1ed85b6309eb3cad/main.py#L78) `--use_milp` to `False` to use `DFS`.
## Load the scene in Unity
1. Install [Unity](https://unity.com/download) and select the editor version `2020.3.25f1`.
2. Clone [AI2-THOR repository](https://github.com/allenai/ai2thor) and switch to the appropriate AI2-THOR commit.
```
git clone https://github.com/allenai/ai2thor.git
git checkout 07445be8e91ddeb5de2915c90935c4aef27a241d
```
3. Reinstall some packages:
```
pip uninstall Werkzeug
pip uninstall Flask
pip install Werkzeug==2.0.1
pip install Flask==2.0.1
```
3. Load `ai2thor/unity` as project in Unity and open `ai2thor/unity/Assets/Scenes/Procedural/Procedural.unity`.
4. In the terminal, run [this python script](connect_to_unity.py):
```
python connect_to_unity --scene <SCENE_JSON_FILE_PATH>
```
5. Press the play button (the triangle) in Unity to view the scene.
## Citation
Please cite the following paper if you use this code in your work.
```bibtex
@InProceedings{Yang_2024_CVPR,
author = {Yang, Yue and Sun, Fan-Yun and Weihs, Luca and VanderBilt, Eli and Herrasti, Alvaro and Han, Winson and Wu, Jiajun and Haber, Nick and Krishna, Ranjay and Liu, Lingjie and Callison-Burch, Chris and Yatskar, Mark and Kembhavi, Aniruddha and Clark, Christopher},
title = {Holodeck: Language Guided Generation of 3D Embodied AI Environments},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2024},
pages = {16227-16237}
}
```
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Ikko Eltociear Ashimine · Japan
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:82bf03b18ffc74d3, topic:large-language-models
matched fp:82bf03b18ffc74d3, topic:text-to-3d