Xingang Pan, Xiaohang Zhan, Bo Dai, Dahua Lin, Chen Change Loy, Ping Luo, "Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation", ECCV2020 (Oral)
Video: https://youtu.be/p7ToqtwfVko
DGP exploits the image prior of an off-the-shelf GAN for various image restoration and manipulation.
Image restoration:
Image manipulation:
A learned prior helps internal learning:
-
python>=3.6
-
pytorch>=1.0.1
-
others
pip install -r requirements.txt
Before start, please download the pretrained BigGAN at Google drive or Baidu cloud (password: uqtw), and put them to pretrained
folder.
Example1: run image colorization example:
sh experiments/examples/run_colorization.sh
The results will be saved in experiments/examples/images
and experiments/examples/image_sheet
.
Example2: process images with an image list:
sh experiments/examples/run_inpainting_list.sh
Example3: evaluate on 1k ImageNet validation images via distributed training based on slurm:
# need to specifiy the root path of imagenet validate set in --root_dir
sh experiments/imagenet1k_128/colorization/train_slurm.sh
Note:
- BigGAN needs a class condition as input. If no class condition is provided, it would be chosen from a set of random samples.
- The hyperparameters provided may not be optimal, feel free to tune them.
The code of BigGAN is borrowed from https://github.com/ajbrock/BigGAN-PyTorch.
@inproceedings{pan2020dgp,
author = {Pan, Xingang and Zhan, Xiaohang and Dai, Bo and Lin, Dahua and Loy, Chen Change and Luo, Ping},
title = {Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2020}
}
@ARTICLE{pan2020dgp_pami,
author={Pan, Xingang and Zhan, Xiaohang and Dai, Bo and Lin, Dahua and Loy, Chen Change and Luo, Ping},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation},
year={2021},
volume={},
number={},
pages={1-1},
doi={10.1109/TPAMI.2021.3115428}
}