  We propose an attention-based networks for transferring motions between
arbitrary objects. Given a source image(s) and a driving video, our networks
animate the subject in the source images according to the motion in the driving
video. In our attention mechanism, dense similarities between the learned
keypoints in the source and the driving images are computed in order to
retrieve the appearance information from the source images. Taking a different
approach from the well-studied warping based models, our attention-based model
has several advantages. By reassembling non-locally searched pieces from the
source contents, our approach can produce more realistic outputs. Furthermore,
our system can make use of multiple observations of the source appearance (e.g.
front and sides of faces) to make the results more accurate. To reduce the
training-testing discrepancy of the self-supervised learning, a novel
cross-identity training scheme is additionally introduced. With the training
scheme, our networks is trained to transfer motions between different subjects,
as in the real testing scenario. Experimental results validate that our method
produces visually pleasing results in various object domains, showing better
performances compared to previous works.
