  Face swapping has both positive applications such as entertainment,
human-computer interaction, etc., and negative applications such as DeepFake
threats to politics, economics, etc. Nevertheless, it is necessary to
understand the scheme of advanced methods for high-quality face swapping and
generate enough and representative face swapping images to train DeepFake
detection algorithms. This paper proposes the first Megapixel level method for
one shot Face Swapping (or MegaFS for short). Firstly, MegaFS organizes face
representation hierarchically by the proposed Hierarchical Representation Face
Encoder (HieRFE) in an extended latent space to maintain more facial details,
rather than compressed representation in previous face swapping methods.
Secondly, a carefully designed Face Transfer Module (FTM) is proposed to
transfer the identity from a source image to the target by a non-linear
trajectory without explicit feature disentanglement. Finally, the swapped faces
can be synthesized by StyleGAN2 with the benefits of its training stability and
powerful generative capability. Each part of MegaFS can be trained separately
so the requirement of our model for GPU memory can be satisfied for megapixel
face swapping. In summary, complete face representation, stable training, and
limited memory usage are the three novel contributions to the success of our
method. Extensive experiments demonstrate the superiority of MegaFS and the
first megapixel level face swapping database is released for research on
DeepFake detection and face image editing in the public domain. The dataset is
at this link.
