While zero-shot appearance-based 3D gaze estimation offers significant cost-efficiency by directly mapping RGB images to gaze vectors, its reliability in Human-Robot Interaction (HRI) settings remains uncertain. Existing benchmarks frequently overlook fundamental HRI conditions, such as dynamic camera viewpoints and moving targets in video. Furthermore, current cross-dataset evaluations often suffer from a complexity gap, where methods trained on diverse datasets are tested on significantly smaller and less varied sets, failing to assess true robustness. To bridge these gaps, we introduce Gaze4HRI, a large-scale dataset (50+ subjects, 3,000+ videos, 600,000+ frames) designed to evaluate state-of-the-art performance against critical HRI variables: illumination, head-gaze conflict, as well as the motion of camera and gaze target in video. Our benchmark reveals that all evaluated methods fail in at least one condition, identifying steeply-downward gaze as a universal failure point. Notably, PureGaze trained on the ETH-X-Gaze dataset uniquely maintains resilience across all other conditions. These results challenge the recent focus in the literature on complex spatial-temporal modeling and Transformer-based architectures. Instead, our findings suggest that extensive data diversity, as exemplified by the ETH-X-Gaze dataset, serves as the primary driver of zero-shot robustness in unconstrained environments, while resilience-enhancing frameworks, such as PureGaze's self-adversarial loss for gaze feature purification, provide a substantial further improvement. Ultimately, this study establishes a rigorous benchmark that provides practical guidelines for practitioners as well as reshaping future research. The dataset and codes are available at https://gazeforhri.github.io.

翻译：尽管基于外观的零样本三维凝视估计通过直接将RGB图像映射为凝视向量具有显著的成本效益，但其在人机交互场景中的可靠性仍不明确。现有基准测试常忽视人机交互的基本条件，例如视频中的动态相机视角与移动目标。此外，跨数据集评估常因复杂度差异而失效——基于多样化数据集训练的方法被测试于规模更小、变化更少的数据集，难以评估真实鲁棒性。为弥合这些差距，我们提出Gaze4HRI——一个大规模数据集（含50余名被试、3000余段视频、60万余帧），旨在针对人机交互关键变量（光照、头姿-凝视冲突、以及视频中相机与凝视目标的运动）评估最先进方法的性能。基准测试表明：所有评估方法至少在某项条件中失效，其中陡峭向下凝视被识别为通用失效点。值得注意的是，基于ETH-X-Gaze数据集训练的PureGaze在其余所有条件下均保持鲁棒性。这些结果挑战了近期文献对复杂时空建模与Transformer架构的重视。相反，我们的发现表明：以ETH-X-Gaze数据集为代表的广泛数据多样性，是开放环境中零样本鲁棒性的主要驱动因素；而诸如PureGaze用于凝视特征纯化的自对抗损失等韧性增强框架，则可提供进一步的显著改进。最终，本研究建立了严格的基准测试，既为从业者提供实用指南，亦为未来研究方向重塑奠定基础。数据集与代码发布于https://gazeforhri.github.io。