Revisiting Latent Space of GAN Inversion for Real Image Editing
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 combine it with highly
capable latent spaces to build combined spaces that faithfully invert real
images while maintaining the quality of edited images. More specifically, we
propose $\mathcal{F}/\mathcal{Z}^{+}$ space consisting of two subspaces:
$\mathcal{F}$ space of an intermediate feature map of StyleGANs enabling
faithful reconstruction and $\mathcal{Z}^{+}$ space of an extended StyleGAN
prior supporting high editing quality. 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.