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• Store test images in /test_code/test_images
• Run /test_code/cartoonize.py
• Results will be saved in /test_code/cartoonized_images

• Place your training data in corresponding folders in /dataset
• Run pretrain.py, results will be saved in /pretrain folder
• Run train.py, results will be saved in /train_cartoon folder
• Codes are cleaned from production environment and untested
• There may be minor problems but should be easy to resolve
• Pre-trained VGG_19 model can be found at following url: https://drive.google.com/file/d/1j0jDENjdwxCDb36meP6-u5xDBzmKBOjJ/view?usp=sharing

• Due to copyright issues, we cannot provide cartoon images used for training.
• However, these training datasets are easy to prepare
• Scenery images are collected from Shinkai Makoto, Miyazaki Hayao and Hosoda Mamoru films
• Clip films into frames and random crop and resize to 256x256
• Portrait images are from Kyoto animations and PA Works
• We use this repo(https://github.com/nagadomi/lbpcascade_animeface) to detect facial areas
• Manual data cleaning will greatly increace both datasets quality

If you use this code for your research, please cite paper:

@InProceedings{Wang_2020_CVPR, author = {Wang, Xinrui and Yu, Jinze}, title = {Learning to Cartoonize Using White-Box Cartoon Representations}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2020} }

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