Semi-Synthetic Dataset Augmentation for Application-Specific Gaze
  Estimation
Cedric Leblond-Menard Gabriel Picard-Krashevski Sofiane Achiche
Although the number of gaze estimation datasets is growing, the application
of appearance-based gaze estimation methods is mostly limited to estimating the
point of gaze on a screen. This is in part because most datasets are generated
in a similar fashion, where the gaze target is on a screen close to camera's
origin. In other applications such as assistive robotics or marketing research,
the 3D point of gaze might not be close to the camera's origin, meaning models
trained on current datasets do not generalize well to these tasks. We therefore
suggest generating a textured tridimensional mesh of the face and rendering the
training images from a virtual camera at a specific position and orientation
related to the application as a mean of augmenting the existing datasets. In
our tests, this lead to an average 47% decrease in gaze estimation angular
error.