Egocentric Gaze Estimation via Neck-Mounted Camera

• URL: http://arxiv.org/abs/2602.11669v1
• Date: Thu, 12 Feb 2026 07:41:27 GMT
• Title: Egocentric Gaze Estimation via Neck-Mounted Camera
• Authors: Haoyu Huang, Yoichi Sato,
• Abstract summary: This paper introduces neck-mounted view gaze estimation, a new task that estimates user gaze from the neck-mounted camera perspective.<n>We collect the first dataset for this task, consisting of approximately 4 hours of video collected from 8 participants during everyday activities.<n>We propose two extensions: an auxiliary gaze out-of-bound classification task and a multi-view co-learning approach that jointly trains head-view and neck-view models.
• Score: 27.513961366278455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces neck-mounted view gaze estimation, a new task that estimates user gaze from the neck-mounted camera perspective. Prior work on egocentric gaze estimation, which predicts device wearer's gaze location within the camera's field of view, mainly focuses on head-mounted cameras while alternative viewpoints remain underexplored. To bridge this gap, we collect the first dataset for this task, consisting of approximately 4 hours of video collected from 8 participants during everyday activities. We evaluate a transformer-based gaze estimation model, GLC, on the new dataset and propose two extensions: an auxiliary gaze out-of-bound classification task and a multi-view co-learning approach that jointly trains head-view and neck-view models using a geometry-aware auxiliary loss. Experimental results show that incorporating gaze out-of-bound classification improves performance over standard fine-tuning, while the co-learning approach does not yield gains. We further analyze these results and discuss implications for neck-mounted gaze estimation.

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