[CVPR2024] Adversarial Score Distillation: When score distillation meets GAN

Arxiv | Paper | Project page

🎉 We released the training codes using 3DGS.

💡 Tips: Camera parameters are crucial for the final results of 3DGS.

Overview (see Project page for more examples)

Generated 3D NeRFs

Generated 3D Gaussians

Installation

Install ASD requirements, Differential Gaussian Rasterization and simple-knn

git clone https://github.com/2y7c3/ASD
cd ASD

### for 3D Gaussian Splatting
git clone --recursive https://github.com/YixunLiang/diff-gaussian-rasterization
pip install ./diff-gaussian-rasterization

git clone https://github.com/YixunLiang/simple-knn.git
pip install ./simple-knn

pip install -r requirements.txt

Optional

pip install ninja

Install tiny-cuda-nn

For 3D Gaussian Splatting

Install Point-E

Install Shape-E

Download finetuned Shap-E by Cap3D, and put it in ./load

Quick start

# NeRF Training
python launch.py --config configs/test_nerf.yaml --train --gpu 0 system.prompt_processor.prompt="A delicious hamburger"

3D gaussian Training (experimental implementation)
python launch.py --config configs/test_gs.yaml --train --gpu 0 system.prompt_processor.prompt="A delicious hamburger"

# Tuning
# you might want to resume training from the certain checkpoint
python launch.py --config configs/test_tune_{nerf or gs}.yaml --train --gpu 0 system.prompt_processor.prompt="A delicious hamburger" resume="path/to/ckpt"

# Testing 
# you can change camera parameters on here
python launch.py --config configs/test_tune_{nerf or gs}.yaml --test --gpu 0 system.prompt_processor.prompt="A delicious hamburger" resume="path/to/ckpt"

Todo

• Release the training codes for NeRF
• Release the training codes for 3DGS
• Release the training codes for 2D images and image editing

Citation

If you find our work useful in your research, please consider citing:

@InProceedings{Wei_2024_CVPR,
    author    = {Wei, Min and Zhou, Jingkai and Sun, Junyao and Zhang, Xuesong},
    title     = {Adversarial Score Distillation: When score distillation meets GAN},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {8131-8141}
}

Acknowledgements

This code is built on many research works and open-source projects:

• gaussian-splatting
• diff-gaussian-rasterization
• Stable-Dreamfusion
• ThreeStudio
• LucidDreamer

Thanks for their excellent works.