This is the official implementation of VICA-NeRF.
Recommented: Follow the instructions on the NeRFStudio.
Directly install:
conda create --name vica -y python=3.8 -y
conda activate vica
pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
pip install ninja git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
pip install nerfstudio==1.0.0 torchtyping
git clone https://github.com/Dongjiahua/VICA-NeRF
cd VICA-NeRF
pip install --upgrade pip setuptools
pip install -e .
To edit the NeRF, it should be firstly trained on a scene. Following the instructions on NeRFStudio, it can be trained by:
ns-train nerfacto --data {PROCESSED_DATA_DIR}
The model will be saved in outputs/.../nerfstudio_models. You can use the following command to edit the NeRF:
ns-train vica --data {PROCESSED_DATA_DIR} --load-dir {outputs/.../nerfstudio_models} --pipeline.prompt {"Instruction"}
Our method supports great controll on the final result by 2D edits. The following options can be used:
--pipeline.control {bool} # Whether to mannuly set the target content. Default: False
--pipeline.warm_up_iters {int} # The number of iterations to warm up the model. Default: -1 (automatically set the warm up iterations)
--pipeline.post_refine {bool} # Whether to refine the result by post-refinement. Default: False. May hurt the result for large scene.
One previous work Instruct-NeRF2NeRF give great suggestions on some tips for editing hyper-parameters. Please refer to their suggestions for better results.
Our code is integrated based on the code framework of Instruct-NeRF2NeRF, thanks to their wonderful work
@inproceedings{dong2023vica,
title={ViCA-NeRF: View-Consistency-Aware 3D Editing of Neural Radiance Fields},
author={Dong, Jiahua and Wang, Yu-Xiong},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023}
}
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