  Face reenactment aims to animate a source face image to a different pose and
expression provided by a driving image. Existing approaches are either designed
for a specific identity, or suffer from the identity preservation problem in
the one-shot or few-shot scenarios. In this paper, we introduce a method for
one-shot face reenactment, which uses the reconstructed 3D meshes (i.e., the
source mesh and driving mesh) as guidance to learn the optical flow needed for
the reenacted face synthesis. Technically, we explicitly exclude the driving
face's identity information in the reconstructed driving mesh. In this way, our
network can focus on the motion estimation for the source face without the
interference of driving face shape. We propose a motion net to learn the face
motion, which is an asymmetric autoencoder. The encoder is a graph
convolutional network (GCN) that learns a latent motion vector from the meshes,
and the decoder serves to produce an optical flow image from the latent vector
with CNNs. Compared to previous methods using sparse keypoints to guide the
optical flow learning, our motion net learns the optical flow directly from 3D
dense meshes, which provide the detailed shape and pose information for the
optical flow, so it can achieve more accurate expression and pose on the
reenacted face. Extensive experiments show that our method can generate
high-quality results and outperforms state-of-the-art methods in both
qualitative and quantitative comparisons.
