Accurate Gaze Estimation using an Active-gaze Morphable Model

TL;DR

This paper introduces an active-gaze 3D morphable model that leverages 3D facial geometry to improve gaze estimation accuracy, especially with low-resolution inputs, and achieves state-of-the-art results on the Eyediap dataset.

Contribution

The paper presents a novel active-gaze 3DMM that integrates 3D facial geometry with a vergence model, enhancing gaze estimation accuracy and applicability without needing gaze origin ground truth.

Findings

Achieves state-of-the-art results on Eyediap dataset.

Performs well with low-resolution inputs.

Can learn with minimal ground truth data.

Abstract

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.