Manifold Alignment for Semantically Aligned Style Transfer

git clone https://github.com/NJUHuoJing/MAST.git

cd MAST
chmod 777 scripts/prepare_data.sh
scripts/prepare_data.sh

conda create -n mast-env python=3.6
conda activate mast-env
pip install -r requirements.txt

# If you want to use post smoothing as the same as PhotoWCT, then install the requirements below;
# You can also just skip it to use fast post smoothing, remember to change cfg.TEST.PHOTOREALISTIC.FAST_SMOOTHING=true
pip install -U setuptools
pip install cupy
pip install pynvrtc

# not use seg
python test_artistic.py --cfg_path configs/config.yaml --content_path data/default/content/4.png --style_path data/default/style/4.png --output_dir results/test/default

# use --content_seg_path and --style_seg_path to user edited style transfer
python test_artistic.py --cfg_path configs/config.yaml --content_path data/default/content/4.png --style_path data/default/style/4.png --output_dir results/test/default --content_seg_path data/default/content_segmentation/4.png --style_seg_path data/default/style_segmentation/4.png --seg_type labelme --resize 512

# not use seg
python test_photorealistic.py --cfg_path configs/config.yaml --content_path data/photo_data/content/in1.png --style_path data/photo_data/style/tar1.png --output_dir results/test/photo --resize 512

# or use --content_seg_path and --style_seg_path to user edited style transfer
python test_photorealistic.py --cfg_path configs/config.yaml --content_path data/photo_data/content/in1.png --style_path data/photo_data/style/tar1.png --output_dir results/test/photo --content_seg_path data/photo_data/content_segmentation/in1.png --style_seg_path data/photo_data/style_segmentation/tar1.png --seg_type dpst --resize 512

python test_gui.py --cfg_path configs/config.yaml

@inproceedings{huo2021manifold,
    author = {Jing Huo and Shiyin Jin and Wenbin Li and Jing Wu and Yu-Kun Lai and Yinghuan Shi and Yang Gao},
    title = {Manifold Alignment for Semantically Aligned Style Transfer},
    booktitle = {IEEE International Conference on Computer Vision},
    pages     = {14861-14869},
    year = {2021}
}