  The objective of this paper is a neural network model that controls the pose
and expression of a given face, using another face or modality (e.g. audio).
This model can then be used for lightweight, sophisticated video and image
editing.
  We make the following three contributions. First, we introduce a network,
X2Face, that can control a source face (specified by one or more frames) using
another face in a driving frame to produce a generated frame with the identity
of the source frame but the pose and expression of the face in the driving
frame. Second, we propose a method for training the network fully
self-supervised using a large collection of video data. Third, we show that the
generation process can be driven by other modalities, such as audio or pose
codes, without any further training of the network.
  The generation results for driving a face with another face are compared to
state-of-the-art self-supervised/supervised methods. We show that our approach
is more robust than other methods, as it makes fewer assumptions about the
input data. We also show examples of using our framework for video face
editing.
