提出轻量级眼动估计模型，实测在多数指标上超越人类。
PAGE: Towards Practical Human-level Gaze Target Estimation
- 显式建模场景与头部特征的复杂交互，提升空间推理能力。
- 在9项指标中7项超越人类表现，剩余2项差距缩小超60%。
- 模型可压缩为轻量版，适合机器人和消费设备部署。
眼动目标估计旨在预测人注视场景中的位置，对理解人类注意力与意图至关重要。该任务需融合全局场景语义理解与人体外观（如姿态、眼球方向）的精细空间推理，现有模型难以达到人类水平，限制了实际应用。为此，我们提出PaGE（Practical Gaze Estimator），通过显式建模场景与头部特征的复杂交互，实现更精准的眼动预测。采用大尺寸ViT-H+骨干网络作为教师模型，在更大更多样化的无标签数据集上蒸馏出轻量化学生模型。架构改进与创新训练策略使PaGE在多个眼动估计任务中达到领先性能：在9个评估指标中，有7项超越人类表现，其余2项的人机差距至少缩小60%。蒸馏后的学生模型保留了教师模型的大部分性能，同时具备轻量化优势，适用于机器人及消费级设备的实际部署。代码与模型检查点可在项目主页获取。
原文摘要 · Abstract (English)
Gaze target estimation, the task of predicting where a person is looking in a scene, is crucial to understanding human attention and intent. It is a challenging task that combines high-level understanding of global scene semantics and precise spatial reasoning using human appearance (e.g. pose, eye orientation). As a result, human-level performance remains elusive for existing models, limiting their practical application. To this end, we propose PaGE (Practical Gaze Estimator), a gaze estimation model that explicitly models the complex interaction between scene and head features. Using a PaGE model with a large ViT-H+ backbone as the teacher, we further distill student models with lighter backbones on a much larger and more diverse unlabeled dataset. The architectural improvements and novel training recipe allow PaGE to achieve state-of-the-art performance on several gaze estimation tasks, outperforming humans in 7 out of 9 metrics while reducing the human-AI gap by at least 60% in the remaining 2. The distilled student models retain most of the teacher's performance while being lightweight enough for practical deployment on robots and consumer devices. The code and model checkpoints are available at our project page.
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