VCNet: a robust approach to blind image inpainting

by Yi Wang, Ying-Cong Chen, Xin Tao, and Jiaya Jia. The training & testing specifications will be updated.

Introduction

This repository gives the implementation of our method in ECCV 2020 paper, 'VCNet: a robust approach to blind image inpainting' (supplementary file). It studies how to repair images with unknown contaminations automatically.

Blind inpainting is a task to automatically complete visual contents without specifying masks for missing areas in an image. Previous work assumes known missing-region-pattern, limiting the application scope. We instead relax the assumption by defining a new blind inpainting setting, making training a neural system robust against various unknown missing region patterns.

Prerequisites

Python3.5 (or higher)
Tensorflow 1.4 (or later versions, excluding 2.x) with NVIDIA GPU or CPU
OpenCV
numpy
scipy
easydict

For tensorflow implementations

Pretrained models

FFHQ-HQ_p256 trained with stroke masks. (Password: ted9)

CelebA-HQ_p256 trained with stroke masks. (Password: 7dzs)

Acknowledgments

Our code benefits a lot from pix2pixHD and Generative Image Inpainting with Contextual Attention.

Citation

If our method is useful for your research, please consider citing:

@article{wang2020vcnet,
    title={VCNet: A Robust Approach to Blind Image Inpainting},
    author={Wang, Yi and Chen, Ying-Cong and Tao, Xin and Jia, Jiaya},
    journal={arXiv preprint arXiv:2003.06816},
    year={2020}
}

Contact

Please send email to yiwang@cse.cuhk.edu.hk.

shepnerd/blindinpainting_vcnet