GazeShift: Unsupervised Gaze Estimation and Dataset for VR
Gaze estimation is instrumental in modern virtual reality (VR) systems. Despite significant progress in remote-camera gaze estimation, VR gaze research remains constrained by data scarcity, particularly the lack of large-scale, accurately labeled datasets captured with the off-axis camera configurations typical of modern headsets. Gaze annotation is difficult since fixation on intended targets cannot be guaranteed. To address these challenges, we introduce VRGaze, the first large-scale off-axis gaze estimation dataset for VR, comprising 2.1 million near-eye infrared images collected from 68 participants. We further propose GazeShift, an attention-guided unsupervised framework for learning...
| Authors | Gil Shapira, Ishay Goldin, Evgeny Artyomov et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 March 2026 |
| Last updated at source | 24 March 2026 |
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