  Although face swapping has attracted much attention in recent years, it
remains a challenging problem. The existing methods leverage a large number of
data samples to explore the intrinsic properties of face swapping without
taking into account the semantic information of face images. Moreover, the
representation of the identity information tends to be fixed, leading to
suboptimal face swapping. In this paper, we present a simple yet efficient
method named FaceSwapper, for one-shot face swapping based on Generative
Adversarial Networks. Our method consists of a disentangled representation
module and a semantic-guided fusion module. The disentangled representation
module is composed of an attribute encoder and an identity encoder, which aims
to achieve the disentanglement of the identity and the attribute information.
The identity encoder is more flexible and the attribute encoder contains more
details of the attributes than its competitors. Benefiting from the
disentangled representation, FaceSwapper can swap face images progressively. In
addition, semantic information is introduced into the semantic-guided fusion
module to control the swapped area and model the pose and expression more
accurately. The experimental results show that our method achieves
state-of-the-art results on benchmark datasets with fewer training samples. Our
code is publicly available at https://github.com/liqi-casia/FaceSwapper.
