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}
}