CA-GAN

We provide PyTorch implementation for CA-GAN and SCA-GAN.

Paper "Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs"

[Paper@IEEE] [Project@Github] [Paper@arxiv] [Project Page]

Generator Architecture of CA-GAN

Stacked CA-GAN (SCA-GAN)

Sample Result

left: sketch synthesis; right: photo synthesis

(a)Input Image, (b)cGAN, (c)CA-GAN, (d)SCA-GAN

Prerequisites

• Linux or similar environment
• Python 2.7
• NVIDIA GPU + CUDA CuDNN

Getting Started

Installation

• Clone this repo:

  git clone https://github.com/fei-hdu/ca-gan
  cd ca-gan

  ​

• Install PyTorch 0.4+ and torchvision from http://pytorch.org and other dependencies (e.g., visdom and dominate). You can install all the dependencies by

  pip install -r requirments.txt

ca-gan train/test

• Download a dataset(CUFS split train and test with this files)
• Download the VGG-Face model. Here we convert torch weight to pyTorch to fit our frame, you can download our converted model directly: Google Drive
• Get face parsing
  • here we use Face Labling to get face parsing
  • Check out the new parsing branch to get the our newly used
• Train a model

  python main.py --model_vgg {model path}

• Test the model

  python test.py --dataroot {data path} --fold {epoch number}

  • The option fold is used for load ./checkpoint/netG_epoch_'+fold+'.weight and you can edit it in test.py

Pre-trained models

• A face $photo \mapsto sketch$ model pre-trained on the CUSF:
  • CA-GAN： [Google Drive] [Baidu Cloud](pwd：tu9l)
  • SCA-GAN (code & model): [Google Drive] [BaiDu Cloud] (pwd: 6dxk)
  • SCA-GAN：A face photo2sketch model and a face sketch2photo model pre-trained on the CUFS[BaiDu Cloud](pwd:2xz5) uses new parsing network(Bisenet)[BaiDu Cloud](pwd:tqek)
• The pre-trained model need to be save at ./checkpoint and named it as netG_epoch_'+fold+'.weight
• Then you can test the model

Datasets

• CUFS
• CUFSF

Result

• Our final result with new parsing can be downloaded: Google Drive

Training/Test Tips

Best practice for training and testing your models. Feel free to ask any questions about coding. Xingxin Xu, [email protected]

Citation

If you find this useful for your research, please cite our paper as:

@article{gao2020ca-gan,
	title = {Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs},
	author = {Jun Yu and Xingxin Xu and Fei Gao and Shengjie Shi and Meng Wang and Dacheng Tao and and Qingming Huang},
	booktitle = {IEEE Transactions on Cybernatics},
        doi = {10.1109/TCYB.2020.2972944},
	year = {2020},
	url = {https://github.com/fei-hdu/ca-gan},
}

Acknowledgments

• Our code is inspired by the pytorch-CycleGAN-and-pix2pix repository.
• This work is greatly supported by Nannan Wang and Chunlei Peng. (HIT@Xidian University)