PVEye: A Large Posture-Variant Eye Tracking Dataset for Head-Mounted AR Devices

Eye tracking technology, essential for enhancing user experience in virtual reality (VR) and augmented reality (AR) devices, has been widely incorporated into advanced head-mounted devices like the Apple Vision Pro and PICO 4 Pro, becoming a standard feature. However, dedicated eye tracking datasets for such devices are severely lacking, with existing datasets commonly facing issues like camera skew and low resolution, particularly failing to adequately consider the diversity in wearing postures. To address this gap, we have developed the Posture-Variant Eye Tracking Dataset (PVEye), which includes 11,044,800 high-resolution near-eye images from 104 participants, showcasing a rich variety of wearing postures. This dataset aims to advance the development and application of appearance-based eye tracking methods. Utilizing this dataset, our evaluations demonstrate that the appearance-based method, particularly the NVGaze model, provides improved accuracy and robustness compared to the traditional feature-based method. Crucially, our experiments indicate that variations in wearing posture can significantly impact eye tracking performance, with posture-related errors contributing approximately 45% to the overall error variance. Moreover, the study delves into the specific impact of calibration and other critical factors on eye tracking performance, offering insights for further optimization of tracking effectiveness.