Unpaired Portrait Drawing Generation via Asymmetric Cycle Mapping
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.
Our Proposed Framework
Sample Results
From left to right: input, output(style1), output(style2), output(style3)
Prerequisites
- Linux or macOS
- Python 3
- CPU or NVIDIA GPU + CUDA CuDNN
Installation
- To install the dependencies, run
pip install -r requirements.txt
Colab
A colab demo is here.
Test steps (apply a pretrained model)
-
- Download pre-trained models from BaiduYun(extract code:c9h7) or GoogleDrive and rename the folder to
checkpoints
.
- Download pre-trained models from BaiduYun(extract code:c9h7) or GoogleDrive and rename the folder to
-
- Test for example photos: generate artistic portrait drawings for example photos in the folder
./examples
using
- Test for example photos: generate artistic portrait drawings for example photos in the folder
# 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.
-
- To test on your own photos: First use an image editor to crop the face region of your photo (or use an optional preprocess here). Then specify the folder that contains test photos using option
--dataroot
, specify save folder name using option--savefolder
and run the above command again:
- To test on your own photos: First use an image editor to crop the face region of your photo (or use an optional preprocess here). Then specify the folder that contains test photos using option
# 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 [email protected] for any questions.
Train steps
-
- Prepare for the dataset: 1) download face photos and portrait drawings from internet (e.g. resources). 2) align, crop photos and drawings & 3) prepare nose, eyes, lips masks according to preprocess instructions. 3) put aligned photos under
./datasets/portrait_drawing/train/A
, aligned drawings under./datasets/portrait_drawing/train/B
, masks underA_nose
,A_eyes
,A_lips
,B_nose
,B_eyes
,B_lips
respectively.
- Prepare for the dataset: 1) download face photos and portrait drawings from internet (e.g. resources). 2) align, crop photos and drawings & 3) prepare nose, eyes, lips masks according to preprocess instructions. 3) put aligned photos under
-
- Train a 3-class style classifier and extract the 3-dim style feature (according to paper). And save the style feature of each drawing in the training set in .npy format, in folder
./datasets/portrait_drawing/train/B_feat
- Train a 3-class style classifier and extract the 3-dim style feature (according to paper). And save the style feature of each drawing in the training set in .npy format, in folder
A subset of our training set is here.
-
- Train our model
sh ./scripts/train.sh
Models are saved in folder checkpoints/portrait_drawing
Citation
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}
}
Acknowledgments
Our code is inspired by pytorch-CycleGAN-and-pix2pix.