  Face swapping aims to generate swapped images that fuse the identity of
source faces and the attributes of target faces. Most existing works address
this challenging task through 3D modelling or generation using generative
adversarial networks (GANs), but 3D modelling suffers from limited
reconstruction accuracy and GANs often struggle in preserving subtle yet
important identity details of source faces (e.g., skin colors, face features)
and structural attributes of target faces (e.g., face shapes, facial
expressions). This paper presents Face Transformer, a novel face swapping
network that can accurately preserve source identities and target attributes
simultaneously in the swapped face images. We introduce a transformer network
for the face swapping task, which learns high-quality semantic-aware
correspondence between source and target faces and maps identity features of
source faces to the corresponding region in target faces. The high-quality
semantic-aware correspondence enables smooth and accurate transfer of source
identity information with minimal modification of target shapes and
expressions. In addition, our Face Transformer incorporates a multi-scale
transformation mechanism for preserving the rich fine facial details. Extensive
experiments show that our Face Transformer achieves superior face swapping
performance qualitatively and quantitatively.
