Abstract
With advancements in AI, new gaze estimation methods are exceeding state-of-the-art (SOTA) benchmarks, but their real-world application reveals a gap with commercial eye-tracking solutions. Factors like model size, inference time, and privacy often go unaddressed. Meanwhile, webcam-based eye-tracking methods lack sufficient accuracy, in particular due to head movement. To tackle these issues, we introduce We bEyeTrack, a framework that integrates lightweight SOTA gaze estimation models directly in the browser. It incorporates model-based head pose estimation and on-device few-shot learning with as few as nine calibration samples (k < 9). WebEyeTrack adapts to new users, achieving SOTA performance with an error margin of 2.32 cm on GazeCapture and real-time inference speeds of 2.4 milliseconds on an iPhone 14. Our open-source code is available at this https URL.
Abstract (translated)
随着人工智能的发展，新的凝视估计方法正在超越现有的最佳水平（SOTA）基准，但它们在实际应用中与商用眼球追踪解决方案之间存在差距。模型大小、推理时间以及隐私等问题往往未被充分考虑。同时，基于网络摄像头的眼球追踪方法由于头部移动等原因，准确性不足。为了解决这些问题，我们推出了WebEyeTrack框架，它将轻量级的SOTA凝视估计模型直接集成到浏览器中。该框架结合了基于模型的头部姿态估算，并使用最少九个校准样本（k<9）进行设备上的少量学习。WebEyeTrack能够适应新用户，在GazeCapture数据集上实现了误差为2.32厘米的最佳性能，并在iPhone 14上达到了每帧2.4毫秒的实时推理速度。我们的开源代码可在此链接获取：[此URL]（请将方括号中的文本替换为实际链接）。
URL
https://arxiv.org/abs/2508.19544
