PyTorch implementation for face sketch synthesis in the wild through semi-supervised learning. Here is an example:

Semi-Supervised Learning for Face Sketch Synthesis in the Wild.
Chaofeng Chen, Wei Liu, Xiao Tan, Kwan-Yee K. Wong.

• Pytorch 0.3
• torchvision 0.2
• opencv-python
• matlab_wrapper
• Matlab (For FSIM evaluation)

• We use CUFS and CUFSF dataset provided by Chinese University of Hong Kong (CUHK) to train our networks. You can also download them from HERE.
• For the training of in the wild images, we use a subset of VGG-Face.

Download the datasets and pretrained models using the following scripts.

bash download_data_models.sh

Download the precalculated features for fast patch matching.

bash download_feature.sh

If the server is not available, you can also download the resources from BaiduYun or GoogleDrive, and then extract the files to the corresponding directory according to the download scripts above.

After download the datasets and pretrain models, use the provided script to test the model

python test.py 1  # Test on CUFS test set
python test.py 2  # Test on CUFSF test set
python test.py 3  # Test on CUHK_Student test set
python test.py 4  # Test on VGG test set

You can also test on your own test dataset. Simply change the --test_dir and --test_weight_path. If you have ground truth images, you can also specify --test_gt_dir.

1. Configure training process.

• vgg_select_num [0 or 10]. 0: no extra images in training. 10: extra VGG-Face in training. Only the largest vgg10 is provided here.
• train_style [cufs, cufsf]. use cufs or cufsf as the reference style.

The models in the paper are trained under 3 configurations:

• --vgg_select_num 0 --train_style cufs. Model evaluated on CUFS.
• --vgg_select_num 0 --train_style cufsf. Model evaluated on CUFSF.
• --vgg_select_num 10 --train_style cufs. Model evaluated on VGG-Face.

2. Train the model.

python train.py

If you find this code or the provided data useful in your research, please consider cite:

@inproceedings{chen2018face-sketch-wild,
    title={Semi-Supervised Learning for Face Sketch Synthesis in the Wild},
    author={Chen, Chaofeng and Liu, Wei and Tan, Xiao and Wong, KKY},
    booktitle={Asian Conference on Computer Vision (ACCV)},
    year={2018},
}

[1] Random Sampling and Locality Constraint for Face Sketch Synthesis
[2] Real-Time Exemplar-Based Face Sketch Synthesis