Photorealistic Style Transfer via Wavelet Transforms | paper | supplementary materials | video stylization results

Jaejun Yoo*, Youngjung Uh*, Sanghyuk Chun*, Byeonkyu Kang, Jung-Woo Ha

Clova AI Research, NAVER (* equal contributions)

PyTorch implementation for photorealistic style transfer that does not need any further post-processing steps; e.g. from day to sunset, from summer to winter, etc. This is the first end-to-end model that can stylize 1024×1024 resolution image in 4.7 seconds, giving a pleasing and photorealistic quality without any post-processing.

The code was written by Jaejun Yoo and Byeongkyu Kang.

• PyTorch >= 0.4.1
• Check the requirements.txt

pip install -r requirements.txt

• Clone this repo:

git clone https://github.com/clovaai/WCT2.git
cd WCT2

• Pretrained models can be found in the ./model_checkpoints

• Prepare image dataset
  • Images can be found in DPST repo
    • You can find the entire content and style images (with paired segmentation label maps) in the following link DPST images. input folder has the content images and the style folder has the style images. Every segmention map can be found in the segmentation folder.
  • To make a new dataset with label pairs, please follow the instruction of PhotoWCT repo
  • Put the content and style images with their segment label pairs (if available) into the example folder accordingly.
    • Currently there are several example images so that you can execute the code as soon as you clone this repo.
• Finally, test the model:

python transfer.py --option_unpool cat5 -a --content ./examples/content --style ./examples/style --content_segment ./examples/content_segment --style_segment ./examples/style_segment/ --output ./outputs/ --verbose --image_size 512 

The test results will be saved to ./outputs by default.

• --content: FOLDER-PATH-TO-CONTENT-IMAGES
• --content_segment: FOLDER-PATH-TO-CONTENT-SEGMENT-LABEL-IMAGES
• --style: FOLDER-PATH-TO-STYLE-IMAGES
• --style_segment: FOLDER-PATH-TO-STYLE-SEGMENT-LABEL-IMAGES
• --output: FOLDER-PATH-TO-OUTPUT-IMAGES
• --image_size: output image size
• --alpha: alpha determines the blending ratio between content and stylized features
• --option_unpool: two versions of our model (sum, cat5)
• -e, --transfer_at_encoder: stylize at the encoder module
• -d, --transfer_at_decoder: stylize at the decoder module
• -s, --transfer_at_skip: stylize at the skipped high frequency components
• -a, --transfer_all: stylize and save for every composition; i.e. power set of {-e,-d,-s})
• --cpu: run on CPU
• --verbose

• DPST: "Deep Photo Style Transfer" | Paper | Code
• PhotoWCT: "A Closed-form Solution to Photorealistic Image Stylization" | Paper | Code
• PhotoWCT (full): PhotoWCT + post processing

• Only for option_unpool = sum version
• Full stylization

python transfer.py --option_unpool sum -e -s --content ./examples/content --style ./examples/style --content_segment ./examples/content_segment --style_segment ./examples/style_segment/ --output ./outputs/ --verbose --image_size 512

• Low-frequency-only stylization

python transfer.py --option_unpool sum -e --content ./examples/content --style ./examples/style --content_segment ./examples/content_segment --style_segment ./examples/style_segment/ --output ./outputs/ --verbose --image_size 512

• option_unpool = cat5 version

python transfer.py --option_unpool cat5 -a --content ./examples/content --style ./examples/style --content_segment ./examples/content_segment --style_segment ./examples/style_segment/ --output ./outputs/ --verbose --image_size 512

• Our implementation is highly inspired from NVIDIA's PhotoWCT Code.

If you find this work useful for your research, please cite:

@inproceedings{yoo2019photorealistic,  
  title={Photorealistic Style Transfer via Wavelet Transforms},
  author={Yoo, Jaejun and Uh, Youngjung and Chun, Sanghyuk and Kang, Byeongkyu and Ha, Jung-Woo},
  booktitle = {International Conference on Computer Vision (ICCV)},
  year={2019}
}

Feel free to contact me if there is any question (Jaejun Yoo [email protected]).

Copyright (c) 2019 NAVER Corp.

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