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[CVPR 2023] StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields
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This repository contains a pytorch implementation for the paper: StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields. StyleRF is an innovative 3D style transfer technique that achieves superior 3D stylization quality with precise geometry reconstruction and it can generalize to various new styles in a zero-shot manner.
Installation
Tested on Ubuntu 20.04 + Pytorch 1.12.1
Install environment:
conda create -n StyleRF python=3.9
conda activate StyleRF
pip install torch torchvision
pip install tqdm scikit-image opencv-python configargparse lpips imageio-ffmpeg kornia lpips tensorboard
Datasets
Please put the datasets in ./data
. You can put the datasets elsewhere if you modify the corresponding paths in the configs.
3D scene datasets
Style image dataset
Quick Start
We provide some trained checkpoints in: StyleRF checkpoints
Then modify the following attributes in scripts/test_style.sh
:
--config
: chooseconfigs/llff_style.txt
orconfigs/nerf_synthetic_style.txt
according to which type of dataset is being used--datadir
: dataset's path--ckpt
: checkpoint's path--style_img
: reference style image's path
To generate stylized novel views:
bash scripts/test_style.sh [GPU ID]
The rendered stylized images can then be found in the directory under the checkpoint's path.
Training
Current settings in
configs
are tested on one NVIDIA RTX A5000 Graphics Card with 24G memory. To reduce memory consumption, you can setbatch_size
,chunk_size
orpatch_size
to a smaller number.
We follow the following 3 steps of training:
1. Train original TensoRF
This step is for reconstructing the density field, which contains more precise geometry details compared to mesh-based methods. You can skip this step by directly downloading pre-trained checkpoints provided by TensoRF checkpoints.
The configs are stored in configs/llff.txt
and configs/nerf_synthetic.txt
. For the details of the settings, please also refer to TensoRF. The checkpoints are stored in ./log
by default.
You can train the original TensoRF by:
bash script/train.sh [GPU ID]
2. Feature grid training stage
This step is for reconstructing the 3D gird containing the VGG features.
The configs are stored in configs/llff_feature.txt
and configs/nerf_synthetic_feature.txt
, in which ckpt
specifies the checkpoints trained in the first step. The checkpoints are stored in ./log_feature
by default.
Then run:
bash script/train_feature.sh [GPU ID]
3. Stylization training stage
This step is for training the style transfer modules.
The configs are stored in configs/llff_style.txt
and configs/nerf_synthetic_style.txt
, in which ckpt
specifies the checkpoints trained in the second step. The checkpoints are stored in ./log_style
by default.
Then run:
bash script/train_style.sh [GPU ID]
Training on 360 Unbounded Scenes
The code for training StyleRF on the Tanks&Temples dataset is available on the 360
branch. To access it, run git checkout 360
.
Acknowledgments
This repo is heavily based on the TensoRF. Thank them for sharing their amazing work!
Citation
If you find our code or paper helps, please consider citing:
@inproceedings{liu2023stylerf,
title={StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields},
author={Liu, Kunhao and Zhan, Fangneng and Chen, Yiwen and Zhang, Jiahui and Yu, Yingchen and El Saddik, Abdulmotaleb and Lu, Shijian and Xing, Eric P},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={8338--8348},
year={2023}
}