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Link to article: https://medium.com/towards-artificial-intelligence/an-insiders-guide-to-cartoonization-using-machine-learning-ce3648adfe8
Tensorflow implementation for CVPR2020 paper “Learning to Cartoonize Using White-box Cartoon Representations.
It is implementation of White Box Cartoonization with some minor tweaks.
- 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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