Tensorflow implementation of Deshpande et al. "Learning Diverse Image Colorization"
The code is tested for Tensorflow-v1.0.1 and python-2.7. The code additionally needs numpy, scipy, scikit-learn and caffe-r1.0 (caffe only for Zhang et al. colorization network).
Fetch data by
bash get_data.sh
Fetch Zhang et al. colorization network for MDN features by
bash get_zhang_colorization.sh
Execute run_lfw.sh to first train vae+mdn and then, generate results for LFW
bash run_lfw.sh
Execute run_demo.sh to get diverse colorization for any image, the model is trained on imagenet
bash run_demo.sh
If you use this code, please cite
@inproceedings{DeshpandeLDColor17,
author = {Aditya Deshpande, Jiajun Lu, Mao-Chuang Yeh, Min Jin Chong and David Forsyth},
title = {Learning Diverse Image Colorization},
booktitle={Computer Vision and Pattern Recognition},
url={https://arxiv.org/abs/1612.01958},
year={2017}
}
Some examples of diverse colorizations on LFW, LSUN Church and ImageNet-Val dataset
Some examples of diverse colorizations for images in the wild, model is trained on imagenet
