Learning to Cartoonize Using White-box Cartoon Representations
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Tensorflow implementation for CVPR2020 paper “Learning to Cartoonize Using White-box Cartoon Representations”.
This repo in under construction, now inference code is available, training code will be updated soon
Use cases
Scenery
Food
Indoor Scenes
People
More Images Are Shown In The Supplementary Materials
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
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
Citation
If you use this code for your research, please cite our paper:
Bib file coming soon.
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