  We propose a novel architecture for GAN inversion, which we call
Feature-Style encoder. The style encoder is key for the manipulation of the
obtained latent codes, while the feature encoder is crucial for optimal image
reconstruction. Our model achieves accurate inversion of real images from the
latent space of a pre-trained style-based GAN model, obtaining better
perceptual quality and lower reconstruction error than existing methods. Thanks
to its encoder structure, the model allows fast and accurate image editing.
Additionally, we demonstrate that the proposed encoder is especially
well-suited for inversion and editing on videos. We conduct extensive
experiments for several style-based generators pre-trained on different data
domains. Our proposed method yields state-of-the-art results for style-based
GAN inversion, significantly outperforming competing approaches. Source codes
are available at https://github.com/InterDigitalInc/FeatureStyleEncoder .
