Abstract
Deep learning has bolstered gaze estimation techniques, but real-world deployment has been impeded by inadequate training datasets. This problem is exacerbated by both hardware-induced variations in eye images and inherent biological differences across the recorded participants, leading to both feature and pixel-level variance that hinders the generalizability of models trained on specific datasets. While synthetic datasets can be a solution, their creation is both time and resource-intensive. To address this problem, we present a framework called Light Eyes or "LEyes" which, unlike conventional photorealistic methods, only models key image features required for video-based eye tracking using simple light distributions. LEyes facilitates easy configuration for training neural networks across diverse gaze-estimation tasks. We demonstrate that models trained using LEyes outperform other state-of-the-art algorithms in terms of pupil and CR localization across well-known datasets. In addition, a LEyes trained model outperforms the industry standard eye tracker using significantly more cost-effective hardware. Going forward, we are confident that LEyes will revolutionize synthetic data generation for gaze estimation models, and lead to significant improvements of the next generation video-based eye trackers.
Abstract (translated)
深度学习已经加强了视点估计技术,但在实际部署方面,缺乏训练数据的问题一直阻碍着进展。这个问题由硬件引起的眼睛图像变异以及记录参与者之间的固有生物学差异加剧,导致特征和像素级别的差异,从而妨碍了特定数据集训练模型的泛化能力。虽然合成数据可以是一种解决方案,但创建它们既需要时间又需要资源。为了解决这一问题,我们提出了名为“光眼”或“LEyes”的框架,它与传统的逼真方法不同,仅使用简单的光照分布来模型关键图像特征,以用于视频跟踪的眼睛定位。LEyes方便地配置用于训练多种视点估计任务神经网络。我们证明,使用LEyes训练的模型在著名的数据集上的 pupils 和 CR 定位方面优于其他最先进的算法。此外,使用更经济实惠的硬件训练的LEyes模型比行业标准眼跟踪器表现更好。未来,我们有信心,LEyes将彻底改变合成数据生成用于视点估计模型,并导致下一代视频式眼跟踪器的重大改进。
URL
https://arxiv.org/abs/2309.06129
