Accurate Gaze Estimation using an Active-gaze Morphable Model
This work addresses gaze estimation for human-computer interaction, offering a method that learns without ground truth gaze origin points, making it more applicable but incremental.
The paper tackles gaze estimation by introducing an active-gaze 3D morphable model that improves accuracy and handles lower-resolution inputs, achieving state-of-the-art results on the Eyediap dataset.
Rather than regressing gaze direction directly from images, we show that adding a 3D shape model can: i) improve gaze estimation accuracy, ii) perform well with lower resolution inputs and iii) provide a richer understanding of the eye-region and its constituent gaze system. Specifically, we use an `eyes and nose' 3D morphable model (3DMM) to capture the eye-region 3D facial geometry and appearance and we equip this with a geometric vergence model of gaze to give an `active-gaze 3DMM'. We show that our approach achieves state-of-the-art results on the Eyediap dataset and we present an ablation study. Our method can learn with only the ground truth gaze target point and the camera parameters, without access to the ground truth gaze origin points, thus widening the applicability of our approach compared to other methods.