[CVPR 2024] GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting
Yiwen Chen*1,2,
Zilong Chen*3,
Chi Zhang2,
Feng Wang3,
Xiaofeng Yang2,
Yikai Wang3,
Zhongang Cai4
Lei Yang4
Huaping Liu3
Guosheng Lin**1,2
*Equal contribution.
**Corresponding author.
1S-Lab, Nanyang Technological University,
2School of Computer Science and Engineering, Nanyang Technological University,
3Department of Computer Science and Technology, Tsinghua University,
4SenseTime Research,
https://github.com/buaacyw/GaussianEditor/assets/52091468/10740174-3208-4408-b519-23f58604339e
https://github.com/buaacyw/GaussianEditor/assets/52091468/44797174-0242-4c82-a383-2d7b3d4fd693
https://github.com/buaacyw/GaussianEditor/assets/52091468/18dd3ef2-4066-428a-918d-c4fe673d0af8
Our environment has been tested on Ubuntu 22, CUDA 11.8 with 3090, A5000 and A6000.
git clone https://github.com/buaacyw/GaussianEditor.git && cd GaussianEditor
# (Option one) Install by conda
conda env create -f environment.yaml
# (Option two) You can also install by pip
# CUDA version 11.7
pip install torch==2.0.1+cu117 torchvision==0.15.2+cu117 --extra-index-url https://download.pytorch.org/whl/cu117
# CUDA version 11.8
pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
# (Option three) If the below two options fail, please try this:
# For CUDA 11.8
bash install.sh
mkdir extern && cd extern
git clone https://github.com/heheyas/viser
pip install -e viser
cd ..
sh download_wonder3d.sh
Please be aware that our WebUI is currently in a beta version. Powered by Viser, you can use our WebUI even if you are limited to remote server. For details, please follow WebUI Guide.
The demand for 3D editing is very diverse. For instance, if you only want to change textures and materials or significantly modify geometry, it's clear that a one-size-fits-all hyperparameter won't work. Therefore, we cannot provide a default hyperparameter setting that works effectively in all scenarios. Therefore, if your results do not meet expectations, please refer to our hyperparameter tuning document. In it, we detail the function of each hyperparameter and advise on which parameters to adjust when you encounter specific issues.
We also provide a command line version of GaussianEditor. Like WebUI, you need to specify your path to the pretrained Gaussians and COLMAP outputs as mentioned in here.
Please check scripts in sciprt
folder. Simply change data.source
to your COLMAP output directory and
system.gs_source
to your pretrained Gaussians and run our demo scripts.
The repo is still being under construction, thanks for your patience.
Our code is based on these wonderful repos: