  StyleGAN models show editing capabilities via their semantically
interpretable latent organizations which require successful GAN inversion
methods to edit real images. Many works have been proposed for inverting images
into StyleGAN's latent space. However, their results either suffer from low
fidelity to the input image or poor editing qualities, especially for edits
that require large transformations. That is because low-rate latent spaces lose
many image details due to the information bottleneck even though it provides an
editable space. On the other hand, higher-rate latent spaces can pass all the
image details to StyleGAN for perfect reconstruction of images but suffer from
low editing qualities. In this work, we present a novel image inversion
architecture that extracts high-rate latent features and includes a flow
estimation module to warp these features to adapt them to edits. The flows are
estimated from StyleGAN features of edited and unedited latent codes. By
estimating the high-rate features and warping them for edits, we achieve both
high-fidelity to the input image and high-quality edits. We run extensive
experiments and compare our method with state-of-the-art inversion methods.
Qualitative metrics and visual comparisons show significant improvements.

Computer Vision and Pattern Recognition