Revisiting Latent Space of GAN Inversion for Real Image Editing

TL;DR

This paper introduces a new latent space combining features of StyleGANs to improve real image inversion and editing quality, addressing the trade-off between reconstruction fidelity and editability.

Contribution

It proposes the $\\mathcal{F}/\mathcal{Z}^{+}$ space, enhancing real image inversion and semantic editing without quality loss, outperforming existing latent spaces.

Findings

The new space preserves reconstruction quality during editing.

It outperforms $\\mathcal{W}$, $\\mathcal{W}^{+}$, and $\\mathcal{S}$ spaces.

Reduces distortion in edited images.

Abstract

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.