  In this paper, we evaluate a synthetic framework to be used in the field of
gaze estimation employing deep learning techniques. The lack of sufficient
annotated data could be overcome by the utilization of a synthetic evaluation
framework as far as it resembles the behavior of a real scenario. In this work,
we use U2Eyes synthetic environment employing I2Head datataset as real
benchmark for comparison based on alternative training and testing strategies.
The results obtained show comparable average behavior between both frameworks
although significantly more robust and stable performance is retrieved by the
synthetic images. Additionally, the potential of synthetically pretrained
models in order to be applied in user's specific calibration strategies is
shown with outstanding performances.
