3D gaze estimation is about predicting the line of sight of a person in 3D
space. Person-independent models for the same lack precision due to anatomical
differences of subjects, whereas person-specific calibrated techniques add
strict constraints on scalability. To overcome these issues, we propose a novel
technique, Facial Landmark Heatmap Activated Multimodal Gaze Estimation
(FLAME), as a way of combining eye anatomical information using eye landmark
heatmaps to obtain precise gaze estimation without any person-specific
calibration. Our evaluation demonstrates a competitive performance of about 10%
improvement on benchmark datasets ColumbiaGaze and EYEDIAP. We also conduct an
ablation study to validate our method.

本研究提出了一种新的技术 ——Facial Landmark Heatmap Activated Multimodal Gaze Estimation (FLAME)，通过使用眼部解剖信息结合眼睛地标热图来获得精确的注视估计，从而克服了人体解剖差异的问题，而不需要任何人特定的标定。研究评估表明，本方法在 ColumbiaGaze 和 EYEDIAP 的基准数据集上具有竞争性能，并进行消融研究以验证方法的有效性。