We provide PyTorch implementations for our CVPR 2020 paper "Unpaired Portrait Drawing Generation via Asymmetric Cycle Mapping". paper, suppl.
This project generates multi-style artistic portrait drawings from face photos using a GAN-based model.
From left to right: input, output(style1), output(style2), output(style3)
pip install -r requirements.txt
A colab demo is here.
checkpoints
../examples
using
# with GPU
python test_seq_style.py
# without GPU
python test_seq_style.py --gpu -1
The test results will be saved to a html file here: ./results/pretrained/test_200/index3styles.html
.
The result images are saved in ./results/pretrained/test_200/images3styles
,
where real
, fake1
, fake2
, fake3
correspond to input face photo, style1 drawing, style2 drawing, style3 drawing respectively.
--dataroot
, specify save folder name using option --savefolder
and run the above command again:# with GPU
python test_seq_style.py --dataroot [input_folder] --savefolder [save_folder_name]
# without GPU
python test_seq_style.py --gpu -1 --dataroot [input_folder] --savefolder [save_folder_name]
# E.g.
python test_seq_style.py --gpu -1 --dataroot ./imgs/test1 --savefolder 3styles_test1
The test results will be saved to a html file here: ./results/pretrained/test_200/index[save_folder_name].html
.
The result images are saved in ./results/pretrained/test_200/images[save_folder_name]
.
An example html screenshot is shown below:
You can contact email yr16@mails.tsinghua.edu.cn for any questions.
./datasets/portrait_drawing/train/A
, aligned drawings under ./datasets/portrait_drawing/train/B
, masks under A_nose
,A_eyes
,A_lips
,B_nose
,B_eyes
,B_lips
respectively../datasets/portrait_drawing/train/B_feat
A subset of our training set is here.
sh ./scripts/train.sh
Models are saved in folder checkpoints/portrait_drawing
If you use this code for your research, please cite our paper.
@inproceedings{YiLLR20,
title = {Unpaired Portrait Drawing Generation via Asymmetric Cycle Mapping},
author = {Yi, Ran and Liu, Yong-Jin and Lai, Yu-Kun and Rosin, Paul L},
booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition (CVPR '20)},
pages = {8214--8222},
year = {2020}
}
Our code is inspired by pytorch-CycleGAN-and-pix2pix.