  We present the second edition of OpenEDS dataset, OpenEDS2020, a novel
dataset of eye-image sequences captured at a frame rate of 100 Hz under
controlled illumination, using a virtual-reality head-mounted display mounted
with two synchronized eye-facing cameras. The dataset, which is anonymized to
remove any personally identifiable information on participants, consists of 80
participants of varied appearance performing several gaze-elicited tasks, and
is divided in two subsets: 1) Gaze Prediction Dataset, with up to 66,560
sequences containing 550,400 eye-images and respective gaze vectors, created to
foster research in spatio-temporal gaze estimation and prediction approaches;
and 2) Eye Segmentation Dataset, consisting of 200 sequences sampled at 5 Hz,
with up to 29,500 images, of which 5% contain a semantic segmentation label,
devised to encourage the use of temporal information to propagate labels to
contiguous frames. Baseline experiments have been evaluated on OpenEDS2020, one
for each task, with average angular error of 5.37 degrees when performing gaze
prediction on 1 to 5 frames into the future, and a mean intersection over union
score of 84.1% for semantic segmentation. As its predecessor, OpenEDS dataset,
we anticipate that this new dataset will continue creating opportunities to
researchers in eye tracking, machine learning and computer vision communities,
to advance the state of the art for virtual reality applications. The dataset
is available for download upon request at
http://research.fb.com/programs/openeds-2020-challenge/.
