HAGI++：头部辅助的视线插补与生成
HAGI++: Head-Assisted Gaze Imputation and Generation
AI总结：
针对眼动追踪数据缺失问题，提出基于多模态扩散模型的HAGI++方法，利用头部朝向与跨模态依赖关系进行视线插补与生成，在多个数据集上优于现有基线，并显著提升了真实世界眼动记录的完整性与准确性。
摘要：
移动式眼动追踪在捕捉真实世界和扩展现实（XR）环境中的人类视觉注意力方面发挥着至关重要的作用，使其成为从行为研究到人机交互等应用不可或缺的工具。然而，由于眨眼、瞳孔检测错误或照明变化导致的缺失值，为进一步的视线数据分析带来了重大挑战。为了应对这一挑战，我们引入了HAGI++——一种基于多模态扩散的视线数据插补方法，该方法首次使用集成的头部朝向传感器来利用头部和眼部运动之间的固有相关性。HAGI++采用基于transformer的扩散模型来学习眼部和头部表示之间的跨模态依赖关系，并且可以轻松扩展以纳入额外的身体运动。在大规模Nymeria、Ego-Exo4D和HOT3D数据集上的广泛评估表明，HAGI++在视线插补方面始终优于传统的插值方法和基于深度学习的时间序列插补基线。此外，统计分析证实，HAGI++生成的视线速度分布与实际人类视线行为高度匹配，确保了更真实的视线插补。此外，通过纳入从商用可穿戴设备捕获的手腕运动，HAGI++在100%视线数据缺失的极端情况下（纯视线生成）超越了依赖全身动作捕捉的现有方法。我们的方法为真实世界环境中更完整、更准确的眼动记录铺平了道路，并在增强各个应用领域的基于视线的分析和交互方面具有巨大潜力。
英文摘要：
Mobile eye-tracking is crucial for capturing human visual attention in real-world and XR settings, supporting research and human-computer interaction. Yet blinks, pupil-detection errors and lighting changes create missing values that hinder gaze analysis. We present HAGI++, a multi-modal diffusion-based imputation method that, for the first time, leverages integrated head-orientation sensors to exploit the natural correlation between head and eye movements. Using a transformer-based diffusion model, it learns cross-modal dependencies between eye and head data and can additionally incorporate wrist/hand motion when such wearable signals are available. Evaluations on the large-scale Nymeria, Ego-Exo4D and HOT3D datasets show that HAGI++ consistently outperforms traditional interpolation and deep-learning time-series imputation baselines. Statistical analysis confirms that its gaze-velocity distributions closely match real human behaviour, yielding realistic imputations. Even when 100% of gaze data are missing (pure gaze generation), HAGI++ exceeds methods that rely on the visual inputs and the methods rely on full-body motion capture by incorporating wrist motion from commercial wearables. Our approach enables more complete, accurate eye-gaze recordings in real-world contexts, enhancing gaze-based analysis and interaction across many applications. Our code is available at https://git.cai.simtech.uni-stuttgart.de/public-projects/HAGI
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
