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.
Abstract (translated)
这篇论文介绍了颈戴式视角凝视估计，这是一种从颈部佩戴摄像头的角度来估算用户视线的新任务。以往关于第一人称凝视估计的研究主要集中在头戴式摄像头上，并且预测设备佩戴者在摄像机视野内的凝视位置，而其他视角则鲜有探索。为了填补这一空白，我们收集了首个用于该任务的数据集，其中包括来自8名参与者在日常活动中拍摄的约4小时视频资料。 我们在新数据集上评估了一种基于Transformer的凝视估计模型GLC，并提出了两项扩展：辅助的视线超出边界分类任务和多视角协同学习方法，后者通过一种感知几何关系的辅助损失共同训练头部视角和颈部视角的模型。实验结果显示，加入视线超出边界分类任务能够改进标准微调的结果，而协同学习的方法并未带来性能提升。 我们进一步分析了这些结果，并讨论了颈戴式凝视估计技术的意义。
URL
https://arxiv.org/abs/2602.11669
