ShadowGP

This repository includes official codes for "Unsupervised Portrait Shadow Removal via Generative Priors (ACM MM 2021)".

Unsupervised Portrait Shadow Removal via Generative Priors
Yingqing He*, Yazhou Xing*, Tianjia Zhang, Qifeng Chen (* indicates joint first authors)
HKUST

[Paper] [Project Page] [Technical Video (Coming soon)]

In this repository, we propose an unsupervised method for portrait shadow removal, named as ShadowGP. ShadowGP can recover a shadow-free portrait image via single image optimization, without a large paired training dataset, which is expensive to collect and time-consuming to train. Besides, our method can also be extended to facial tattoo removal and watermark removal tasks.

ShadowGP can decompose the single input shadowed portrait image into 3 parts: a full-shadow portrait, a shadow-free portrait and a shadow mask. Blending the three parts together can reconstruct the input shadowed portrait. The decomposed shadow-free portrait is the target output.

Code will come soon.

Citation

@inproceedings{he21unsupervised,
  title     = {Unsupervised Portrait Shadow Removal via Generative Priors},
  author    = {He, Yingqing and Xing, Yazhou and Zhang, Tianjia and Chen, Qifeng},
  booktitle = {ACM International Conference on Multimedia (ACM MM)},
  year      = {2021}
}