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.

本文提出了一种新的深度神经网络架构，采用粗到细的图像变形和像素强度校正的方式，可以处理人脸重定向问题的特定实例，经过数值比较和用户研究表明可行。该架构能够在前向过程中处理所有操作，并以端到端的方式联合学习所有操作的参数。在学习之后，该神经网络能够合成具有操作后视线的图像，而重定向角度可从某一范围内自由选择提供给网络作为输入。