  In this work, we consider the task of generating highly-realistic images of a
given face with a redirected gaze. We treat this problem as a specific instance
of conditional image generation and suggest a new deep architecture that can
handle this task very well as revealed by numerical comparison with prior art
and a user study. Our deep architecture performs coarse-to-fine warping with an
additional intensity correction of individual pixels. All these operations are
performed in a feed-forward manner, and the parameters associated with
different operations are learned jointly in the end-to-end fashion. After
learning, the resulting neural network can synthesize images with manipulated
gaze, while the redirection angle can be selected arbitrarily from a certain
range and provided as an input to the network.
