🎥 [ECCV 2024] GaussCtrl: Multi-View Consistent Text-Driven 3D Gaussian Splatting Editing

Jing Wu^*1 , Jia-Wang Bian^*2 , Xinghui Li¹, Guangrun Wang¹, Ian Reid², Philip Torr¹, Victor Adrian Prisacariu¹
* denotes equal contribution
¹University of Oxford,
²Mohamed bin Zayed University of Artificial Intelligence

✨ News

• [9.4.2024] Our original results utilise stable-diffusion-v1-5 from runwayml for editing, which is now unavailable. Please change the diffusion checkpoint to other available models, e.g. CompVis/stable-diffusion-v1-4, by using --pipeline.diffusion_ckpt "CompVis/stable-diffusion-v1-4". Reproduce our original results by using the checkpoint --pipeline.diffusion_ckpt "jinggogogo/gaussctrl-sd15"

⚙️ Installation

• Tested on CUDA11.8 + Ubuntu22.04 + NeRFStudio1.0.0 (NVIDIA RTX A5000 24G)

Clone the repo.

git clone https://github.com/ActiveVisionLab/gaussctrl.git
cd gaussctrl

1. NeRFStudio and Lang-SAM

conda create -n gaussctrl python=3.8
conda activate gaussctrl
conda install cuda -c nvidia/label/cuda-11.8.0

GaussCtrl is built upon NeRFStudio, follow this link to install NeRFStudio first. If you are failing to build tiny-cuda-nn, try building from scratch, see here. We recommend using NeRFStudio v1.0.0 with gsplat v0.1.3.

pip install nerfstudio==1.0.0

pip install gsplat==0.1.3

Install Lang-SAM for mask extraction.

pip install -U git+https://github.com/luca-medeiros/lang-segment-anything.git

pip install -r requirements.txt

2. Install GaussCtrl

pip install -e .

3. Verify the install

ns-train -h

🗄️ Data

Use Our Preprocessed Data

Our preprocessed data are under the data folder, where

• fangzhou is from NeRF-Art
• bear, face are from Instruct-NeRF2NeRF
• garden is from Mip-NeRF 360
• stone horse and dinosaur are from BlendedMVS

We thank these authors for their great work!

Customize Your Data

We recommend to pre-process your data to 512x512, and following this page to process your data.

▶️ Get Started

1. Train a 3DGS

To get started, you first need to train your 3DGS model. We use splatfacto from NeRFStudio.

ns-train splatfacto --output-dir {output/folder} --experiment-name EXPEIMENT_NAME nerfstudio-data --data {path/to/your/data}

2. Edit your model

Once you finish training the splatfacto model, the checkpoints will be saved to output/folder/EXPEIMENT_NAME folder.

Start editing your model by running:

ns-train gaussctrl --load-checkpoint {output/folder/.../nerfstudio_models/step-000029999.ckpt} --experiment-name EXPEIMENT_NAME --output-dir {output/folder} --pipeline.datamanager.data {path/to/your/data} --pipeline.edit_prompt "YOUR PROMPT" --pipeline.reverse_prompt "PROMPT TO DESCRIBE THE UNEDITED SCENE" --pipeline.guidance_scale 5 --pipeline.chunk_size {batch size of images during editing} --pipeline.langsam_obj 'OBJECT TO BE EDITED' 

Please note that the Lang-SAM is optional here. If you are editing the environment, please remove this argument.

ns-train gaussctrl --load-checkpoint {output/folder/.../nerfstudio_models/step-000029999.ckpt} --experiment-name EXPEIMENT_NAME --output-dir {output/folder} --pipeline.datamanager.data {path/to/your/data} --pipeline.edit_prompt "YOUR PROMPT" --pipeline.reverse_prompt "PROMPT TO DESCRIBE THE UNEDITED SCENE" --pipeline.guidance_scale 5 --pipeline.chunk_size {batch size of images during editing} 

Here, --pipeline.guidance_scale denotes the classifier-free guidance used when editing the images. --pipeline.chunk_size denotes the number of images edited together during 1 batch. We are using NVIDIA RTX A5000 GPU (24G), and the maximum chunk size is 3. (~22G)

Control the number of reference views using --pipeline.ref_view_num, by default, it is set to 4.

Small Tips

• If your editings are not as expected, please check the images edited by ControlNet.
• Normally, conditioning your editing on the good ControlNet editing views is very helpful, which means choosing those good ControlNet editing views as reference views is better.

🔧 Reproduce Our Results

Experiments in the main paper are included in the scripts folder. To reproduce the results, first train the splatfacto model. We take the bear case as an example here.

ns-train splatfacto --output-dir unedited_models --experiment-name bear nerfstudio-data --data data/bear

Then edit the 3DGS by running:

ns-train gaussctrl --load-checkpoint unedited_models/bear/splatfacto/2024-07-10_170906/nerfstudio_models/step-000029999.ckpt --experiment-name bear --output-dir outputs --pipeline.datamanager.data data/bear --pipeline.edit_prompt "a photo of a polar bear in the forest" --pipeline.reverse_prompt "a photo of a bear statue in the forest" --pipeline.guidance_scale 5 --pipeline.chunk_size 3 --pipeline.langsam_obj 'bear' --viewer.quit-on-train-completion True 

In our experiments, We sampled 40 views randomly from the entire dataset to accelerate the method, which is set in gc_datamanager.py by default. We split the entire set into 4 subsets, and randomly sampled 10 images in each subset split. Feel free to decrease/increase the number to see the difference by modifying --pipeline.datamanager.subset-num and --pipeline.datamanager.sampled-views-every-subset. Set --pipeline.datamanager.load-all to True, if you want to edit all the images in the dataset.

📷 View Results Using NeRFStudio Viewer

ns-viewer --load-config {outputs/.../config.yml} 

🎥 Render Your Results

• Render all the dataset views.

ns-gaussctrl-render dataset --load-config {outputs/.../config.yml} --output_path {render/EXPEIMENT_NAME} 

• Render a mp4 of a camera path

ns-gaussctrl-render camera-path --load-config {outputs/.../config.yml} --camera-path-filename data/EXPEIMENT_NAME/camera_paths/render-path.json --output_path render/EXPEIMENT_NAME.mp4

Evaluation

We use this code to evaluate our method.

Citation

If you find this code or find the paper useful for your research, please consider citing:

@article{gaussctrl2024,
author = {Wu, Jing and Bian, Jia-Wang and Li, Xinghui and Wang, Guangrun and Reid, Ian and Torr, Philip and Prisacariu, Victor},
title = {{GaussCtrl: Multi-View Consistent Text-Driven 3D Gaussian Splatting Editing}},
journal = {ECCV},
year = {2024},
}