Revisiting Latent Space of GAN Inversion for Real Image Editing

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 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 F/Z+ space consisting of two subspaces: F space of an intermediate feature map of StyleGANs enabling faithful reconstruction and 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 Z+, enabling semantic editing without sacrificing image quality. Comprehensive experiments show that Z+ can replace the most commonly-used W, W+, and S spaces while preserving reconstruction quality, resulting in reduced distortion of edited images.