This repository is for our CVPR 2024 paper 'MVIP-NeRF: Multi-view 3D Inpainting on NeRF Scenes via Diffusion Prior'.
conda create -n MVIPnerf python=3.8
conda activate MVIPnerf
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
pip install -r requirements.txt
pip install -r requirements_df.txt
pip install lpips
pip install ConfigArgParse
Take SPIn-NeRF dataset as example:
1
├── images
│ ├── IMG_2707.jpg
│ ├── IMG_2708.jpg
│ ├── ...
│ └── IMG_2736.jpg
└── images_4
├── IMG_2707.png
├── IMG_2708.png
├── ...
├── IMG_2736.png
└── label
├── IMG_2707.png
├── IMG_2708.png
├── ...
└── IMG_2736.png
└── Depth
├── IMG_2707.png
├── IMG_2708.png
├── ...
└── IMG_2736.png
Also, for easier usage of the SPIn-NeRF dataset, we have uploaded one example. Note that our method does not rely on explicit 2D inpaintings results, although we provided the inpainted inputs.
python DS_NeRF/run.py --config DS_NeRF/config/config_1.txt
datadir: folder for the dataset
factor: downscale of the image resolution of the inpainted scene
is_normal_guidance: control whether using normal guidance
is_colla_guidance: control whether using multi-view guidance
text: text prompt for the inpainted scene
normalmap_render_factor: we use a factor to downscale the rendered normal map, due to the RAM limitation
- Release the code.
- Release video results.
The repository is based on SPIn-NeRF and stable dreamfusion
This project is licensed under NTU S-Lab License 1.0. Redistribution and use should follow this license.
If you find our MVIP-NeRF useful in your work, please consider citing it:
@inproceedings{MVIPNeRF,
title={MVIP-NeRF: Multi-view 3D Inpainting on NeRF Scenes via Diffusion Prior},
author={Honghua Chen and Chen Change Loy and Xingang Pan},
year={2024},
booktitle={CVPR},
}