  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.

Computer Vision and Pattern Recognition;Graphics