StyleGAN's disentangled style representation enables powerful image editing by manipulating the latent variables, but accurately mapping real-world images to their latent variables (GAN inversion) remains a challenge. Existing GAN inversion methods struggle to maintain editing directions and produce realistic results. To address these limitations, we propose Make It So, a novel GAN inversion method that operates in the Z (noise) space rather than the typical W (latent style) space. Make It So preserves editing capabilities, even for out-of-domain images. This is a crucial property that was overlooked in prior methods. Our quantitative evaluations demonstrate that Make It So outperforms the state-of-the-art method PTI~\cite{roich2021pivotal} by a factor of five in inversion accuracy and achieves ten times better edit quality for complex indoor scenes.
@article{arxiv.2304.14403,
title = {Make It So: Steering StyleGAN for Any Image Inversion and Editing},
author = {Anand Bhattad and Viraj Shah and Derek Hoiem and D. A. Forsyth},
journal= {arXiv preprint arXiv:2304.14403},
year = {2023}
}