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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 $\mathcal{Z}^+$ and integratethem into seminal GAN inversion methods to improve editing quality. Besidesfaithful reconstruction, our extensions achieve sophisticated editing qualitywith the aid of the StyleGAN prior. 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.

Balancing Reconstruction and Editing Quality of GAN Inversion for Real Image Editing with StyleGAN Prior Latent Space