  The StyleGAN family succeed in high-fidelity image generation and allow for
flexible and plausible editing of generated images by manipulating the
semantic-rich latent style space.However, projecting a real image into its
latent space encounters an inherent trade-off between inversion quality and
editability. Existing encoder-based or optimization-based StyleGAN inversion
methods attempt to mitigate the trade-off but see limited performance. To
fundamentally resolve this problem, we propose a novel two-phase framework by
designating two separate networks to tackle editing and reconstruction
respectively, instead of balancing the two. Specifically, in Phase I, a
W-space-oriented StyleGAN inversion network is trained and used to perform
image inversion and editing, which assures the editability but sacrifices
reconstruction quality. In Phase II, a carefully designed rectifying network is
utilized to rectify the inversion errors and perform ideal reconstruction.
Experimental results show that our approach yields near-perfect reconstructions
without sacrificing the editability, thus allowing accurate manipulation of
real images. Further, we evaluate the performance of our rectifying network,
and see great generalizability towards unseen manipulation types and
out-of-domain images.
