Enhancing 3D Gaze Estimation in the Wild using Weak Supervision with Gaze Following Labels

• URL: http://arxiv.org/abs/2502.20249v1
• Date: Thu, 27 Feb 2025 16:35:25 GMT
• Title: Enhancing 3D Gaze Estimation in the Wild using Weak Supervision with Gaze Following Labels
• Authors: Pierre Vuillecard, Jean-Marc Odobez,
• Abstract summary: We introduce a novel Self-Training Weakly-Supervised Gaze Estimation framework (ST-WSGE)<n>We propose the Gaze Transformer (GaT), a modality-agnostic architecture capable of simultaneously learning static and dynamic gaze information from both image and video datasets.<n>By combining 3D video datasets with 2D gaze target labels from gaze following tasks, our approach achieves the following key contributions.
• Score: 10.827081942898506
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate 3D gaze estimation in unconstrained real-world environments remains a significant challenge due to variations in appearance, head pose, occlusion, and the limited availability of in-the-wild 3D gaze datasets. To address these challenges, we introduce a novel Self-Training Weakly-Supervised Gaze Estimation framework (ST-WSGE). This two-stage learning framework leverages diverse 2D gaze datasets, such as gaze-following data, which offer rich variations in appearances, natural scenes, and gaze distributions, and proposes an approach to generate 3D pseudo-labels and enhance model generalization. Furthermore, traditional modality-specific models, designed separately for images or videos, limit the effective use of available training data. To overcome this, we propose the Gaze Transformer (GaT), a modality-agnostic architecture capable of simultaneously learning static and dynamic gaze information from both image and video datasets. By combining 3D video datasets with 2D gaze target labels from gaze following tasks, our approach achieves the following key contributions: (i) Significant state-of-the-art improvements in within-domain and cross-domain generalization on unconstrained benchmarks like Gaze360 and GFIE, with notable cross-modal gains in video gaze estimation; (ii) Superior cross-domain performance on datasets such as MPIIFaceGaze and Gaze360 compared to frontal face methods. Code and pre-trained models will be released to the community.

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