  The advent of foundation models signals a new era in artificial intelligence.
The Segment Anything Model (SAM) is the first foundation model for image
segmentation. In this study, we evaluate SAM's ability to segment features from
eye images recorded in virtual reality setups. The increasing requirement for
annotated eye-image datasets presents a significant opportunity for SAM to
redefine the landscape of data annotation in gaze estimation. Our investigation
centers on SAM's zero-shot learning abilities and the effectiveness of prompts
like bounding boxes or point clicks. Our results are consistent with studies in
other domains, demonstrating that SAM's segmentation effectiveness can be
on-par with specialized models depending on the feature, with prompts improving
its performance, evidenced by an IoU of 93.34% for pupil segmentation in one
dataset. Foundation models like SAM could revolutionize gaze estimation by
enabling quick and easy image segmentation, reducing reliance on specialized
models and extensive manual annotation.
