  Recent work has shown that a variety of semantics emerge in the latent space
of Generative Adversarial Networks (GANs) when being trained to synthesize
images. However, it is difficult to use these learned semantics for real image
editing. A common practice of feeding a real image to a trained GAN generator
is to invert it back to a latent code. However, existing inversion methods
typically focus on reconstructing the target image by pixel values yet fail to
land the inverted code in the semantic domain of the original latent space. As
a result, the reconstructed image cannot well support semantic editing through
varying the inverted code. To solve this problem, we propose an in-domain GAN
inversion approach, which not only faithfully reconstructs the input image but
also ensures the inverted code to be semantically meaningful for editing. We
first learn a novel domain-guided encoder to project a given image to the
native latent space of GANs. We then propose domain-regularized optimization by
involving the encoder as a regularizer to fine-tune the code produced by the
encoder and better recover the target image. Extensive experiments suggest that
our inversion method achieves satisfying real image reconstruction and more
importantly facilitates various image editing tasks, significantly
outperforming start-of-the-arts.
