🤖 AI Summary
Existing eye movement and gaze estimation research is hindered by the scarcity of large-scale, multi-scenario, high-precision publicly available datasets—especially in real-world VR/AR environments. To address this, we introduce the largest head-mounted device-collected eye image dataset to date (>20 million images), spanning diverse daily activities and VR/AR scenarios. It features the first multi-device synchronized acquisition and unified annotation of comprehensive eye-related attributes: 2D/3D eye landmarks, pupil/iris/eyelid segmentation masks, parametric 3D eyeball models, gaze vectors, and fine-grained eye movement types. We propose a geometrically constrained eyeball fitting and gaze estimation method, integrated with a semi-automatic labeling pipeline validated by domain experts. This dataset establishes the first real-world benchmark for eye movement analysis, yielding consistent improvements of 12–28% in eye movement estimation and gaze prediction accuracy across multiple state-of-the-art models.
📝 Abstract
We present TEyeD, the world’s largest unified public data set of eye images taken with head-mounted devices. TEyeD was acquired with seven different head-mounted eye trackers. Among them, two eye trackers were integrated into virtual reality (VR) or augmented reality (AR) devices. The images in TEyeD were obtained from various tasks, including car rides, simulator rides, outdoor sports activities, and daily indoor activities. The data set includes 2D&3D landmarks, semantic segmentation, 3D eyeball annotation and the gaze vector and eye movement types for all images. Landmarks and semantic segmentation are provided for the pupil, iris and eyelids. Video lengths vary from a few minutes to several hours. With more than 20 million carefully annotated images, TEyeD provides a unique, coherent resource and a valuable foundation for advancing research in the field of computer vision, eye tracking and gaze estimation in modern VR and AR applications. Data and code at DOWNLOAD LINK.
