pytorch-CartoonGAN

Pytorch implementation of CartoonGAN [1] (CVPR 2018)

• Parameters without information in the paper were set arbitrarily.
• I used face-cropped celebA (src) and anime (tgt) collected from the web data because I could not find the author's data.

Tensorflow version

CartoonGAN-tensorflow

Usage

1.Download VGG19

VGG19

2.Train

python CartoonGAN.py --name your_project_name --src_data src_data_path --tgt_data tgt_data_path --vgg_model pre_trained_VGG19_model_path

Folder structure

The following shows basic folder structure.

├── data
│   ├── src_data # src data (not included in this repo)
│   │   ├── train 
│   │   └── test
│   └── tgt_data # tgt data (not included in this repo)
│       ├── train 
│       └── pair # edge-promoting results to be saved here
│
├── CartoonGAN.py # training code
├── edge_promoting.py
├── utils.py
├── networks.py
└── name_results # results to be saved here

Resutls

paper results

celebA2anime face

Initialization phase (reconstruction)

Input - Result (this repo)

Cartoonization

• I got the author's results from CaroonGAN-Test-Pytorch-Torch.

Input - Result (this repo)	Author's pre-trained model (Hayao)	Author's pre-trained model (Hosoda)

Development Environment

• NVIDIA GTX 1080 ti
• cuda 8.0
• python 3.5.3
• pytorch 0.4.0
• torchvision 0.2.1
• opencv 3.2.0

Reference

[1] Chen, Yang, Yu-Kun Lai, and Yong-Jin Liu. "CartoonGAN: Generative Adversarial Networks for Photo Cartoonization." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.

(Full paper: http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_CartoonGAN_Generative_Adversarial_CVPR_2018_paper.pdf)