### Install Remaining Python Dependencies

Source: https://github.com/vast-ai-research/detailgen3d/blob/main/README.md

Installs all other required Python packages listed in the 'requirements.txt' file. This command ensures all necessary libraries for running DetailGen3D are present. It completes the setup of the project's software environment.

```bash
pip install -r requirements.txt
```

--------------------------------

### Install PyTorch with Specific CUDA Version

Source: https://github.com/vast-ai-research/detailgen3d/blob/main/README.md

Installs PyTorch and torchvision, essential libraries for deep learning, via pip. Users must replace '{your-cuda-version}' with their specific CUDA toolkit version to ensure GPU acceleration. This step is critical for leveraging the project's computational capabilities.

```bash
pip install torch torchvision --index-url https://download.pytorch.org/whl/{your-cuda-version}
```

--------------------------------

### Create and Activate Conda Environment

Source: https://github.com/vast-ai-research/detailgen3d/blob/main/README.md

Creates a new isolated Conda environment named 'detailgen3d' with Python 3.10. This optional step helps manage project-specific dependencies without conflicts. The environment is then activated, making it ready for dependency installation.

```bash
conda create -n detailgen3d python=3.10
conda activate detailgen3d
```

--------------------------------

### Clone DetailGen3D Project Repository

Source: https://github.com/vast-ai-research/detailgen3d/blob/main/README.md

Clones the DetailGen3D project from GitHub. This command fetches the entire codebase, including scripts and assets, to your local machine. It then changes the current directory into the newly cloned repository.

```bash
git clone https://github.com/VAST-AI-Research/DetailGen3D.git
cd DetailGen3D
```

--------------------------------

### Run DetailGen3D Inference for Geometry Enhancement

Source: https://github.com/vast-ai-research/detailgen3d/blob/main/README.md

Executes the main inference script to enhance a coarse 3D mesh using an input image. The script automatically downloads necessary pretrained model weights from Hugging Face. This command demonstrates how to apply DetailGen3D to your own 3D assets.

```bash
python scripts/inference_detailgen3d.py \
  --mesh_input assets/model/cb7e6c4a-b4dd-483c-9789-3d4887ee7434.glb \
  --image_input assets/image/cb7e6c4a-b4dd-483c-9789-3d4887ee7434.png
```

--------------------------------

### Cite DetailGen3D Research Paper (BibTeX)

Source: https://github.com/vast-ai-research/detailgen3d/blob/main/README.md

Provides the BibTeX entry for citing the DetailGen3D research paper. This citation includes authors, title, publication year, and arXiv details. It is used for academic referencing in research papers and publications.

```bibtex
@misc{deng2025detailgen3dgenerative3dgeometry,
      title={DetailGen3D: Generative 3D Geometry Enhancement via Data-Dependent Flow}, 
      author={Ken Deng and Yuan-Chen Guo and Jingxiang Sun and Zi-Xin Zou and Yangguang Li and Xin Cai and Yan-Pei Cao and Yebin Liu and Ding Liang},
      year={2025},
      eprint={2411.16820},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2411.16820}, 
}
```

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