  Generative Adversarial Networks (GANs) have significantly advanced image
synthesis through mapping randomly sampled latent codes to high-fidelity
synthesized images. However, applying well-trained GANs to real image editing
remains challenging. A common solution is to find an approximate latent code
that can adequately recover the input image to edit, which is also known as GAN
inversion. To invert a GAN model, prior works typically focus on reconstructing
the target image at the pixel level, yet few studies are conducted on whether
the inverted result can well support manipulation at the semantic level. This
work fills in this gap by proposing in-domain GAN inversion, which consists of
a domain-guided encoder and a domain-regularized optimizer, to regularize the
inverted code in the native latent space of the pre-trained GAN model. In this
way, we manage to sufficiently reuse the knowledge learned by GANs for image
reconstruction, facilitating a wide range of editing applications without any
retraining. We further make comprehensive analyses on the effects of the
encoder structure, the starting inversion point, as well as the inversion
parameter space, and observe the trade-off between the reconstruction quality
and the editing property. Such a trade-off sheds light on how a GAN model
represents an image with various semantics encoded in the learned latent
distribution. Code, models, and demo are available at the project page:
https://genforce.github.io/idinvert/.
