The task of manipulating real image attributes through StyleGAN inversion has
been extensively researched. This process involves searching latent variables
from a well-trained StyleGAN generator that can synthesize a real image,
modifying these latent variables, and then synthesizing an image with the
desired edits. A balance must be struck between the quality of the
reconstruction and the ability to edit. Earlier studies utilized the
low-dimensional W-space for latent search, which facilitated effective editing
but struggled with reconstructing intricate details. More recent research has
turned to the high-dimensional feature space F, which successfully inverses the
input image but loses much of the detail during editing. In this paper, we
introduce StyleFeatureEditor -- a novel method that enables editing in both
w-latents and F-latents. This technique not only allows for the reconstruction
of finer image details but also ensures their preservation during editing. We
also present a new training pipeline specifically designed to train our model
to accurately edit F-latents. Our method is compared with state-of-the-art
encoding approaches, demonstrating that our model excels in terms of
reconstruction quality and is capable of editing even challenging out-of-domain
examples. Code is available at
this https URL

通过 StyleGAN 反演技术，我们介绍了一种名为 StyleFeatureEditor 的新方法，可以在 w-latents 和 F-latents 两个潜变量空间中进行编辑，实现了对细节的精确重建和修改。与现有的编码方法相比，我们的模型在重建质量上表现优秀，并能够处理具有挑战性的领域外示例。