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。