Gaze estimation problem tackled through synthetic images
This work addresses the data scarcity issue in gaze estimation for computer vision applications, but it is incremental as it builds on existing synthetic and real datasets.
The paper tackled the gaze estimation problem by using a synthetic image framework to overcome the lack of annotated data, achieving comparable average performance to real benchmarks with more robust and stable results, and showing outstanding performance in user-specific calibration strategies.
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.