  In this paper we are concerned with the challenging problem of producing a
full image sequence of a deformable face given only an image and generic facial
motions encoded by a set of sparse landmarks. To this end we build upon recent
breakthroughs in image-to-image translation such as pix2pix, CycleGAN and
StarGAN which learn Deep Convolutional Neural Networks (DCNNs) that learn to
map aligned pairs or images between different domains (i.e., having different
labels) and propose a new architecture which is not driven any more by labels
but by spatial maps, facial landmarks. In particular, we propose the MotionGAN
which transforms an input face image into a new one according to a heatmap of
target landmarks. We show that it is possible to create very realistic face
videos using a single image and a set of target landmarks. Furthermore, our
method can be used to edit a facial image with arbitrary motions according to
landmarks (e.g., expression, speech, etc.). This provides much more flexibility
to face editing, expression transfer, facial video creation, etc. than models
based on discrete expressions, audios or action units.
