In this short report, we present a simple, yet effective approach to editing real images via generative adversarial networks (GAN). Unlike previous techniques, that treat all editing tasks as an operation that affects pixel values in the entire image in our approach we cut up the image into a set of smaller segments. For those segments corresponding latent codes of a generative network can be estimated with greater accuracy due to the lower number of constraints. When codes are altered by the user the content in the image is manipulated locally while the rest of it remains unaffected. Thanks to this property the final edited image better retains the original structures and thus helps to preserve natural look.
@article{arxiv.2110.06269,
title = {Real Image Inversion via Segments},
author = {David Futschik and Michal Lukáč and Eli Shechtman and Daniel Sýkora},
journal= {arXiv preprint arXiv:2110.06269},
year = {2021}
}