  Recently, a surge of advanced facial editing techniques have been proposed
that leverage the generative power of a pre-trained StyleGAN. To successfully
edit an image this way, one must first project (or invert) the image into the
pre-trained generator's domain. As it turns out, however, StyleGAN's latent
space induces an inherent tradeoff between distortion and editability, i.e.
between maintaining the original appearance and convincingly altering some of
its attributes. Practically, this means it is still challenging to apply
ID-preserving facial latent-space editing to faces which are out of the
generator's domain. In this paper, we present an approach to bridge this gap.
Our technique slightly alters the generator, so that an out-of-domain image is
faithfully mapped into an in-domain latent code. The key idea is pivotal tuning
- a brief training process that preserves the editing quality of an in-domain
latent region, while changing its portrayed identity and appearance. In Pivotal
Tuning Inversion (PTI), an initial inverted latent code serves as a pivot,
around which the generator is fined-tuned. At the same time, a regularization
term keeps nearby identities intact, to locally contain the effect. This
surgical training process ends up altering appearance features that represent
mostly identity, without affecting editing capabilities. We validate our
technique through inversion and editing metrics, and show preferable scores to
state-of-the-art methods. We further qualitatively demonstrate our technique by
applying advanced edits (such as pose, age, or expression) to numerous images
of well-known and recognizable identities. Finally, we demonstrate resilience
to harder cases, including heavy make-up, elaborate hairstyles and/or headwear,
which otherwise could not have been successfully inverted and edited by
state-of-the-art methods.
