AI 中文总结
提出一种物理信息WiFi感知框架，结合多径感知注意力与解耦表示学习，在Person-in-WiFi-3D等基准及实际环境中，显著提升移动跨环境三维人体姿态估计的鲁棒性与泛化性能。
AI 中文摘要
利用商用WiFi信号进行无设备人体姿态估计已成为移动计算系统中普适感知的一种有前景的范式。然而，现有方法在异构环境中部署时，由于复杂的多径传播和无线信号的域偏移，往往会出现严重的性能下降。在本文中，我们提出了一种用于移动及跨环境场景下鲁棒三维人体姿态估计的物理信息WiFi感知框架。我们的方法明确建模了无线信号传播特性，并结合了感知环境相关信号变化的多径感知注意力机制。为进一步提升泛化能力，我们引入了一种解耦表示学习方案，该方案将与姿态相关的特征与环境特定因素分离，无需大量重新训练即可实现有效的跨域适配。我们使用商用WiFi设备实现了该系统，并在多个公共基准上进行了评估，包括Person-in-WiFi-3D和MM-Fi，以及在不同室内环境中的实际部署。实验结果表明，与最先进的方法相比，我们的框架显著提升了鲁棒性和泛化性能，尤其是在跨环境场景下。这些结果凸显了物理信息无线感知在移动计算系统中实现可靠、可扩展且无基础设施的以人为中心应用的潜力。
英文摘要
Device-free human pose estimation using commodity WiFi signals has emerged as a promising paradigm for pervasive sensing in mobile computing systems. However, existing approaches often suffer from severe performance degradation when deployed across heterogeneous environments due to complex multipath propagation and domain shifts in wireless signals. In this paper, we present a physics-informed WiFi sensing framework for robust 3D human pose estimation under mobile and cross-environment settings. Our approach explicitly models wireless signal propagation characteristics and incorporates multipath-aware attention to capture environment-dependent signal variations. To further improve generalization, we introduce a disentangled representation learning scheme that separates pose-related features from environment-specific factors, enabling effective cross-domain adaptation without requiring extensive retraining. We implement our system using commodity WiFi devices and evaluate it on multiple public benchmarks, including Person-in-WiFi-3D and MM-Fi, as well as real-world deployments across diverse indoor environments. Experimental results demonstrate that our framework significantly improves robustness and generalization performance compared to state-of-the-art methods, particularly under cross-environment scenarios. These results highlight the potential of physics-informed wireless sensing for enabling reliable, scalable, and infrastructure-free human-centric applications in mobile computing systems.
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