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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
  • 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:
  • 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

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