Despite the enhanced realism and immersion provided by VR headsets, users frequently encounter adverse effects such as digital eye strain (DES), dry eye, and potential long-term visual impairment due to excessive eye stimulation from VR displays and pressure from the mask. Recent VR headsets are increasingly equipped with eye-oriented monocular cameras to segment ocular feature maps. Yet, to compute the incident light stimulus and observe periocular condition alterations, it is imperative to transform these relative measurements into metric dimensions. To bridge this gap, we propose a lightweight framework derived from the U-Net 3+ deep learning backbone that we re-optimised, to estimate measurable periocular depth maps. Compatible with any VR headset equipped with an eye-oriented monocular camera, our method reconstructs three-dimensional periocular regions, providing a metric basis for related light stimulus calculation protocols and medical guidelines. Navigating the complexities of data collection, we introduce a Dynamic Periocular Data Generation (DPDG) environment based on UE MetaHuman, which synthesises thousands of training images from a small quantity of human facial scan data. Evaluated on a sample of 36 participants, our method exhibited notable efficacy in the periocular global precision evaluation experiment, and the pupil diameter measurement.

翻译：尽管 VR 头显提升了真实感和沉浸感，但用户常因 VR 显示屏的过度眼部刺激和面罩压力而出现数字眼疲劳、干眼症及潜在长期视力损伤等不良反应。当前 VR 头显日益配备面向眼睛的单目摄像头以分割眼部特征图，然而，为计算入射光刺激并观察眼周状态变化，必须将这些相对测量值转换为度量维度。为填补这一空白，我们提出一种轻量级框架，基于我们重新优化的 U-Net 3+ 深度学习主干网络，用于估算可测量的眼周深度图。该方法兼容任何配备面向眼睛的单目摄像头的 VR 头显，可重建三维眼周区域，为相关光刺激计算协议和医疗指南提供度量基础。针对数据收集的复杂性，我们引入基于 UE MetaHuman 的动态眼周数据生成环境，该环境通过少量人类面部扫描数据合成数千张训练图像。在 36 名参与者样本上的评估表明，我们的方法在眼周全局精度评估实验和瞳孔直径测量中展现出显著有效性。