Numerous attempts have been made to the task of person-agnostic face swapping
given its wide applications. While existing methods mostly rely on tedious
network and loss designs, they still struggle in the information balancing
between the source and target faces, and tend to produce visible artifacts. In
this work, we introduce a concise and effective framework named StyleSwap. Our
core idea is to leverage a style-based generator to empower high-fidelity and
robust face swapping, thus the generator's advantage can be adopted for
optimizing identity similarity. We identify that with only minimal
modifications, a StyleGAN2 architecture can successfully handle the desired
information from both source and target. Additionally, inspired by the ToRGB
layers, a Swapping-Driven Mask Branch is further devised to improve information
blending. Furthermore, the advantage of StyleGAN inversion can be adopted.
Particularly, a Swapping-Guided ID Inversion strategy is proposed to optimize
identity similarity. Extensive experiments validate that our framework
generates high-quality face swapping results that outperform state-of-the-art
methods both qualitatively and quantitatively.

本文提出了一种名为 StyleSwap 的简洁而有效的框架，将基于风格的生成器用于高保真和强大的人脸交换，可通过优化标识相似性来实现生成器的优势，并提出了 Swapping-Driven Mask Branch 和 Swapping-Guided ID Inversion 策略来进一步改进信息混合，实验证明本框架可产生高质量的人脸交换结果，优于现有最先进方法。