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。

细节决定成败：适用于细节丰富的 StyleGAN 反向和高质量图像编辑的 StyleFeatureEditor

The Devil is in the Details: StyleFeatureEditor for Detail-Rich StyleGAN  Inversion and High Quality Image Editing

This paper studies the problem of StyleGAN inversion, which plays an
essential role in enabling the pretrained StyleGAN to be used for real image
editing tasks. The goal of StyleGAN inversion is to find the exact latent code
of the given image in the latent space of StyleGAN. This problem has a high
demand for quality and efficiency. Existing optimization-based methods can
produce high-quality results, but the optimization often takes a long time. On
the contrary, forward-based methods are usually faster but the quality of their
results is inferior. In this paper, we present a new feed-forward network
"E2Style" for StyleGAN inversion, with significant improvement in terms of
efficiency and effectiveness. In our inversion network, we introduce: 1) a
shallower backbone with multiple efficient heads across scales; 2) multi-layer
identity loss and multi-layer face parsing loss to the loss function; and 3)
multi-stage refinement. Combining these designs together forms an effective and
efficient method that exploits all benefits of optimization-based and
forward-based methods. Quantitative and qualitative results show that our
E2Style performs better than existing forward-based methods and comparably to
state-of-the-art optimization-based methods while maintaining the high
efficiency as well as forward-based methods. Moreover, a number of real image
editing applications demonstrate the efficacy of our E2Style. Our code is
available at https://github.com/wty-ustc/e2style

该研究针对 StyleGAN 反演问题，提出了一种效率和效果均有显著提升的前向网络 E2Style，通过浅层 backbone、多头设计，多阶段细化等方式，结合了基于优化与前向方法的优点。实验结果表明 E2Style 在效率和结果质量上较现有前向方法有较大优势，并且在保持前向方法高效的同时达到了和基于优化方法同等性能。