DGRGaze: A Difference-Guided Gaze Estimation Framework Based on 6D Rotation Matrix Representation

This is the authors' abstract. We don't add key points for this paper.

Gaze estimation aims to infer a person’s gaze direction from images, which has wide applications in human-computer interaction. Recently, full-face-based gaze estimation methods have become increasingly prominent, owing to their efficiency and adaptability. However, irrelevant information in facial images limits estimation accuracy. To address this challenge, we propose DGRGaze, a difference-guided gaze estimation framework based on 6D rotation matrix representation. By incorporating an auxiliary task, namely predicting the gaze differences between facial image pairs, DGRGaze becomes more sensitive to gaze-related features. Additionally, a novel 6D rotation matrix representation is introduced to enhance the efficiency of network learning and resolve ambiguities in large-angle scenarios. Furthermore, we design a multi-task loss function based on the geodesic distance of the rotation matrix, enabling more precise quantification of gaze differences. Extensive experiments demonstrate our method achieves state-of-the-art performance on benchmark datasets, proving its effectiveness.