  This paper presents a novel Region-Aware Face Swapping (RAFSwap) network to
achieve identity-consistent harmonious high-resolution face generation in a
local-global manner: \textbf{1)} Local Facial Region-Aware (FRA) branch
augments local identity-relevant features by introducing the Transformer to
effectively model misaligned cross-scale semantic interaction. \textbf{2)}
Global Source Feature-Adaptive (SFA) branch further complements global
identity-relevant cues for generating identity-consistent swapped faces.
Besides, we propose a \textit{Face Mask Predictor} (FMP) module incorporated
with StyleGAN2 to predict identity-relevant soft facial masks in an
unsupervised manner that is more practical for generating harmonious
high-resolution faces. Abundant experiments qualitatively and quantitatively
demonstrate the superiority of our method for generating more
identity-consistent high-resolution swapped faces over SOTA methods, \eg,
obtaining 96.70 ID retrieval that outperforms SOTA MegaFS by 5.87$\uparrow$.
