  As an indicator of human attention gaze is a subtle behavioral cue which can
be exploited in many applications. However, inferring 3D gaze direction is
challenging even for deep neural networks given the lack of large amount of
data (groundtruthing gaze is expensive and existing datasets use different
setups) and the inherent presence of gaze biases due to person-specific
difference. In this work, we address the problem of person-specific gaze model
adaptation from only a few reference training samples. The main and novel idea
is to improve gaze adaptation by generating additional training samples through
the synthesis of gaze-redirected eye images from existing reference samples. In
doing so, our contributions are threefold: (i) we design our gaze redirection
framework from synthetic data, allowing us to benefit from aligned training
sample pairs to predict accurate inverse mapping fields; (ii) we proposed a
self-supervised approach for domain adaptation; (iii) we exploit the gaze
redirection to improve the performance of person-specific gaze estimation.
Extensive experiments on two public datasets demonstrate the validity of our
gaze retargeting and gaze estimation framework.
