  In this work, we propose a semantic flow-guided two-stage framework for
shape-aware face swapping, namely FlowFace. Unlike most previous methods that
focus on transferring the source inner facial features but neglect facial
contours, our FlowFace can transfer both of them to a target face, thus leading
to more realistic face swapping. Concretely, our FlowFace consists of a face
reshaping network and a face swapping network. The face reshaping network
addresses the shape outline differences between the source and target faces. It
first estimates a semantic flow (i.e., face shape differences) between the
source and the target face, and then explicitly warps the target face shape
with the estimated semantic flow. After reshaping, the face swapping network
generates inner facial features that exhibit the identity of the source face.
We employ a pre-trained face masked autoencoder (MAE) to extract facial
features from both the source face and the target face. In contrast to previous
methods that use identity embedding to preserve identity information, the
features extracted by our encoder can better capture facial appearances and
identity information. Then, we develop a cross-attention fusion module to
adaptively fuse inner facial features from the source face with the target
facial attributes, thus leading to better identity preservation. Extensive
quantitative and qualitative experiments on in-the-wild faces demonstrate that
our FlowFace outperforms the state-of-the-art significantly.

Computer Vision and Pattern Recognition;Artificial Intelligence