  Recent inversion methods have shown that real images can be inverted into
StyleGAN's latent space and numerous edits can be achieved on those images
thanks to the semantically rich feature representations of well-trained GAN
models. However, extensive research has also shown that image inversion is
challenging due to the trade-off between high-fidelity reconstruction and
editability. In this paper, we tackle an even more difficult task, inverting
erased images into GAN's latent space for realistic inpaintings and editings.
Furthermore, by augmenting inverted latent codes with different latent samples,
we achieve diverse inpaintings. Specifically, we propose to learn an encoder
and mixing network to combine encoded features from erased images with
StyleGAN's mapped features from random samples. To encourage the mixing network
to utilize both inputs, we train the networks with generated data via a novel
set-up. We also utilize higher-rate features to prevent color inconsistencies
between the inpainted and unerased parts. We run extensive experiments and
compare our method with state-of-the-art inversion and inpainting methods.
Qualitative metrics and visual comparisons show significant improvements.
