Remote photoplethysmography (rPPG) measurement enables non-contact physiological monitoring but suffers from accuracy degradation under head motion and illumination changes. Existing deep learning methods are mostly heuristic and lack theoretical grounding, which limits robustness and interpretability. In this work, we propose a physics-informed rPPG paradigm derived from the Navier-Stokes equations of hemodynamics, showing that the pulse signal follows a second-order dynamical system whose discrete solution naturally leads to a causal convolution. This provides a theoretical justification for using a Temporal Convolutional Network (TCN). Based on this principle, we design PHASE-Net, a lightweight model with three key components: (1) Zero-FLOPs Axial Swapper module, which swaps or transposes a few spatial channels to mix distant facial regions and enhance cross-region feature interaction without breaking temporal order; (2) Adaptive Spatial Filter, which learns a soft spatial mask per frame to highlight signal-rich areas and suppress noise; and (3) Gated TCN, a causal dilated TCN with gating that models long-range temporal dynamics for accurate pulse recovery. Extensive experiments demonstrate that PHASE-Net achieves state-of-the-art performance with strong efficiency, offering a theoretically grounded and deployment-ready rPPG solution.

翻译：远程光电容积脉搏波（rPPG）测量可实现非接触式生理监测，但在头部运动和光照变化下存在精度下降的问题。现有的深度学习方法大多基于启发式设计，缺乏理论依据，这限制了其鲁棒性和可解释性。在本工作中，我们提出了一种基于物理信息的rPPG范式，该范式源自血流动力学的纳维-斯托克斯方程，研究表明脉搏信号遵循一个二阶动力系统，其离散解自然导出了一个因果卷积。这为使用时序卷积网络（TCN）提供了理论依据。基于此原理，我们设计了PHASE-Net，这是一个轻量级模型，包含三个关键组件：（1）零FLOPs轴向交换模块，通过交换或转置少量空间通道来混合远距离面部区域，并在不破坏时序顺序的情况下增强跨区域特征交互；（2）自适应空间滤波器，为每帧学习一个软空间掩码，以突出信号丰富的区域并抑制噪声；以及（3）门控TCN，一种带有门控的因果膨胀TCN，用于建模长程时序动态以实现精确的脉搏恢复。大量实验表明，PHASE-Net以强大的效率实现了最先进的性能，提供了一个具有理论依据且可直接部署的rPPG解决方案。