[CVPR2020]Learning to Cartoonize Using White-box Cartoon Representations

Tensorflow implementation for CVPR2020 paper “Learning to Cartoonize Using White-box Cartoon Representations”.
Improved method for facial images are now available:
https://github.com/SystemErrorWang/FacialCartoonization

Use cases

Scenery

Food

Indoor Scenes

People

More Images Are Shown In The Supplementary Materials

Online demo

Some kind people made online demo for this project
Demo link: https://cartoonize-lkqov62dia-de.a.run.app/cartoonize
Code: https://github.com/experience-ml/cartoonize
Sample Demo: https://www.youtube.com/watch?v=GqduSLcmhto&feature=emb_title

Prerequisites

Training code: Linux or Windows
NVIDIA GPU + CUDA CuDNN for performance
Inference code: Linux, Windows and MacOS

How To Use

Installation

Assume you already have NVIDIA GPU and CUDA CuDNN installed
Install tensorflow-gpu, we tested 1.12.0 and 1.13.0rc0
Install scikit-image==0.14.5, other versions may cause problems

Inference with Pre-trained Model

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

Train

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
Pretrained VGG_19 model can be found at following url: https://drive.google.com/file/d/1j0jDENjdwxCDb36meP6-u5xDBzmKBOjJ/view?usp=sharing

Datasets

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

Acknowledgement

We are grateful for the help from Lvmin Zhang and Style2Paints Research

License

license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
Commercial application is prohibited, please remain this license if you clone this repo

Citation

If you use this code for your research, please cite our 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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Gigot-blip/White-box-Cartoonization