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The exploration of the latent space in StyleGANs and GAN inversion exemplifyimpressive real-world image editing, yet the trade-off between reconstructionquality and editing quality remains an open problem. In this study, we revisitStyleGANs' hyperspherical prior $\mathcal{Z}$ and combine it with highlycapable latent spaces to build combined spaces that faithfully invert realimages while maintaining the quality of edited images. More specifically, wepropose $\mathcal{F}/\mathcal{Z}^{+}$ space consisting of two subspaces:$\mathcal{F}$ space of an intermediate feature map of StyleGANs enablingfaithful reconstruction and $\mathcal{Z}^{+}$ space of an extended StyleGANprior supporting high editing quality. We project the real images into theproposed 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 mostcommonly-used $\mathcal{W}$, $\mathcal{W}^{+}$, and $\mathcal{S}$ spaces whilepreserving reconstruction quality, resulting in reduced distortion of editedimages.

Revisiting Latent Space of GAN Inversion for Real Image Editing