  With contemporary advancements of graphics engines, recent trend in deep
learning community is to train models on automatically annotated simulated
examples and apply on real data during test time. This alleviates the burden of
manual annotation. However, there is an inherent difference of distributions
between images coming from graphics engine and real world. Such domain
difference deteriorates test time performances of models trained on synthetic
examples. In this paper we address this issue with unsupervised adversarial
feature adaptation across synthetic and real domain for the special use case of
eye gaze estimation which is an essential component for various downstream HCI
tasks. We initially learn a gaze estimator on annotated synthetic samples
rendered from a 3D game engine and then adapt the features of unannotated real
samples via a zero-sum minmax adversarial game against a domain discriminator
following the recent paradigm of generative adversarial networks. Such
adversarial adaptation forces features of both domains to be indistinguishable
which enables us to use regression models trained on synthetic domain to be
used on real samples. On the challenging MPIIGaze real life dataset, we
outperform recent fully supervised methods trained on manually annotated real
samples by appreciable margins and also achieve 13\% more relative gain after
adaptation compared to the current benchmark method of SimGAN
