  Image animation consists of generating a video sequence so that an object in
a source image is animated according to the motion of a driving video. Our
framework addresses this problem without using any annotation or prior
information about the specific object to animate. Once trained on a set of
videos depicting objects of the same category (e.g. faces, human bodies), our
method can be applied to any object of this class. To achieve this, we decouple
appearance and motion information using a self-supervised formulation. To
support complex motions, we use a representation consisting of a set of learned
keypoints along with their local affine transformations. A generator network
models occlusions arising during target motions and combines the appearance
extracted from the source image and the motion derived from the driving video.
Our framework scores best on diverse benchmarks and on a variety of object
categories. Our source code is publicly available.
