Hybrid-Domain Adaptative Representation Learning for Gaze Estimation

• URL: http://arxiv.org/abs/2511.13222v1
• Date: Mon, 17 Nov 2025 10:38:50 GMT
• Title: Hybrid-Domain Adaptative Representation Learning for Gaze Estimation
• Authors: Qida Tan, Hongyu Yang, Wenchao Du,
• Abstract summary: We present a novel Hybrid-domain Adaptative Representation Learning framework to learn robust gaze representation.<n>We propose to disentangle gaze-relevant representation from low-quality facial images by aligning features extracted from high-quality near-eye images.<n>Experiments on EyeDiap, MPIIFaceGaze, and Gaze360 datasets demonstrate that our approach achieves state-of-the-art accuracy.
• Score: 20.422491630669885
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Appearance-based gaze estimation, aiming to predict accurate 3D gaze direction from a single facial image, has made promising progress in recent years. However, most methods suffer significant performance degradation in cross-domain evaluation due to interference from gaze-irrelevant factors, such as expressions, wearables, and image quality. To alleviate this problem, we present a novel Hybrid-domain Adaptative Representation Learning (shorted by HARL) framework that exploits multi-source hybrid datasets to learn robust gaze representation. More specifically, we propose to disentangle gaze-relevant representation from low-quality facial images by aligning features extracted from high-quality near-eye images in an unsupervised domain-adaptation manner, which hardly requires any computational or inference costs. Additionally, we analyze the effect of head-pose and design a simple yet efficient sparse graph fusion module to explore the geometric constraint between gaze direction and head-pose, leading to a dense and robust gaze representation. Extensive experiments on EyeDiap, MPIIFaceGaze, and Gaze360 datasets demonstrate that our approach achieves state-of-the-art accuracy of $\textbf{5.02}^{\circ}$ and $\textbf{3.36}^{\circ}$, and $\textbf{9.26}^{\circ}$ respectively, and present competitive performances through cross-dataset evaluation. The code is available at https://github.com/da60266/HARL.

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