Awesome

3D-Adapter

Official PyTorch implementation of the papers:

3D-Adapter: Geometry-Consistent Multi-View Diffusion for High-Quality 3D Generation <br> Hansheng Chen¹, Bokui Shen², Yulin Liu^3,4, Ruoxi Shi³, Linqi Zhou², Connor Z. Lin², Jiayuan Gu³, Hao Su^3,4, Gordon Wetzstein¹, Leonidas Guibas¹<br> ¹Stanford University, ²Apparate Labs, ³UCSD, ⁴Hillbot <br> [Project page] [🤗Demo] [Paper]

Generic 3D Diffusion Adapter Using Controlled Multi-View Editing <br> Hansheng Chen¹, Ruoxi Shi², Yulin Liu², Bokui Shen³, Jiayuan Gu², Gordon Wetzstein¹, Hao Su², Leonidas Guibas¹<br> ¹Stanford University, ²UCSD, ³Apparate Labs <br> [Project page] [🤗Demo] [Paper]

https://github.com/user-attachments/assets/6cba3a92-04fe-46ee-88ca-e6dfe5443c36

Todos

• Release GRM-based 3D-Adapters (unfortunately, we cannot release these models before the official release of GRM)

Installation

The code has been tested in the environment described as follows:

• Linux (tested on Ubuntu 20 and above)
• CUDA Toolkit 11.8 and above
• PyTorch 2.1 and above
• FFmpeg, x264 (optional, for exporting videos)

Other dependencies can be installed via pip install -r requirements.txt.

An example of installation commands is shown below (you may change the CUDA version yourself):

# Export the PATH of CUDA toolkit
export PATH=/usr/local/cuda-12.1/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-12.1/lib64:$LD_LIBRARY_PATH

# Create conda environment
conda create -y -n mvedit python=3.10
conda activate mvedit

# Install FFmpeg (optional)
conda install -c conda-forge ffmpeg x264

# Install PyTorch
conda install pytorch==2.1.2 torchvision==0.16.2 pytorch-cuda=12.1 -c pytorch -c nvidia

# Clone this repo and install other dependencies
git clone https://github.com/Lakonik/MVEdit && cd MVEdit
pip install -r requirements.txt

This codebase also works on Windows systems if the environment is configured correctly. Please refer to Issue #8 for more information about the environment setup on Windows.

Inference

We recommend using the Gradio Web UI and its APIs for inference. A GPU with at least 24GB of VRAM is required to run the Web UI.

Web UI

Run the following command to start the Web UI:

python app.py --unload-models

The Web UI will be available at http://localhost:7860. If you add the --share flag, a temporary public URL will be generated for you to share the Web UI with others.

All models will be automatically loaded on demand. The first run will take a very long time to download the models. Check your network connection to GitHub, Google Drive and Hugging Face if the download fails.

To view other options, run:

python app.py -h

API

After starting the Web UI, the API docs will be available at http://localhost:7860/?view=api. The docs are automatically generated by Gradio, and the data types and default values may be incorrect. Please use the default values in the Web UI as a reference.

Please refer to our examples for API usage with python.

Training

Optimization-based 3D-Adapters (a.k.a. MVEdit adapters) adopt only off-the-shelf models and require no further training.

The training code for GRM-based 3D-Adapters will be released after the official release of GRM.

Acknowledgements

This codebase is built upon the following repositories:

• Base library modified from SSDNeRF
• NeRF renderer and DMTet modified from Stable-DreamFusion
• Gaussian Splatting renderer modified from 3DGS and Differential Gaussian Rasterization
• Mesh I/O modified from DreamGaussian
• GRM for Gaussian reconstruction
• Zero123++ for image-to-3D initialization
• IP-Adapter for extra conditioning
• TRACER for background removal
• LoFTR for pose estimation in image-to-3D
• Omnidata for normal prediction in image-to-3D
• Image Packer for mesh preprocessing

Citation

@misc{3dadapter2024,
    title={3D-Adapter: Geometry-Consistent Multi-View Diffusion for High-Quality 3D Generation},
    author={Hansheng Chen and Bokui Shen and Yulin Liu and Ruoxi Shi and Linqi Zhou and Connor Z. Lin and Jiayuan Gu and Hao Su and Gordon Wetzstein and Leonidas Guibas},
    year={2024},
    eprint={2410.18974},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/abs/2410.18974},
}

@misc{mvedit2024,
    title={Generic 3D Diffusion Adapter Using Controlled Multi-View Editing},
    author={Hansheng Chen and Ruoxi Shi and Yulin Liu and Bokui Shen and Jiayuan Gu and Gordon Wetzstein and Hao Su and Leonidas Guibas},
    year={2024},
    eprint={2403.12032},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}