Balancing Reconstruction and Editing Quality of GAN Inversion for Real
  Image Editing with StyleGAN Prior Latent Space
Kai Katsumata Duc Minh Vo Bei Liu Hideki Nakayama
The exploration of the latent space in StyleGANs and GAN inversion exemplify
impressive real-world image editing, yet the trade-off between reconstruction
quality and editing quality remains an open problem. In this study, we revisit
StyleGANs' hyperspherical prior $\mathcal{Z}$ and $\mathcal{Z}^+$ and integrate
them into seminal GAN inversion methods to improve editing quality. Besides
faithful reconstruction, our extensions achieve sophisticated editing quality
with the aid of the StyleGAN prior. We project the real images into the
proposed space to obtain the inverted codes, by which we then move along
$\mathcal{Z}^{+}$, enabling semantic editing without sacrificing image quality.
Comprehensive experiments show that $\mathcal{Z}^{+}$ can replace the most
commonly-used $\mathcal{W}$, $\mathcal{W}^{+}$, and $\mathcal{S}$ spaces while
preserving reconstruction quality, resulting in reduced distortion of edited
images.