  We describe a novel learning-by-synthesis method for estimating gaze
direction of an automated intelligent surveillance system. Recently, progress
in learning-by-synthesis has proposed training models on synthetic images,
which can effectively reduce the cost of manpower and material resources.
However, learning from synthetic images still fails to achieve the desired
performance compared to naturalistic images due to the different distribution
of synthetic images. In an attempt to address this issue, previous method is to
improve the realism of synthetic images by learning a model. However, the
disadvantage of the method is that the distortion has not been improved and the
authenticity level is unstable. To solve this problem, we put forward a new
structure to improve synthetic images, via the reference to the idea of style
transformation, through which we can efficiently reduce the distortion of
pictures and minimize the need of real data annotation. We estimate that this
enables generation of highly realistic images, which we demonstrate both
qualitatively and with a user study. We quantitatively evaluate the generated
images by training models for gaze estimation. We show a significant
improvement over using synthetic images, and achieve state-of-the-art results
on various datasets including MPIIGaze dataset.
