MAGE: A Multi-task Architecture for Gaze Estimation with an Efficient Calibration Module
Journal:
arXiv
Published Date:
May 22, 2025
Abstract
Eye gaze can provide rich information on human psychological activities, and
has garnered significant attention in the field of Human-Robot Interaction
(HRI). However, existing gaze estimation methods merely predict either the gaze
direction or the Point-of-Gaze (PoG) on the screen, failing to provide
sufficient information for a comprehensive six Degree-of-Freedom (DoF) gaze
analysis in 3D space. Moreover, the variations of eye shape and structure among
individuals also impede the generalization capability of these methods. In this
study, we propose MAGE, a Multi-task Architecture for Gaze Estimation with an
efficient calibration module, to predict the 6-DoF gaze information that is
applicable for the real-word HRI. Our basic model encodes both the directional
and positional features from facial images, and predicts gaze results with
dedicated information flow and multiple decoders. To reduce the impact of
individual variations, we propose a novel calibration module, namely
Easy-Calibration, to fine-tune the basic model with subject-specific data,
which is efficient to implement without the need of a screen. Experimental
results demonstrate that our method achieves state-of-the-art performance on
the public MPIIFaceGaze, EYEDIAP, and our built IMRGaze datasets.
