PHASE-Net：面向高效远程光电容积脉搏波测量的物理锚定谐波注意力系统
PHASE-Net: Physics-Grounded Harmonic Attention System for Efficient Remote Photoplethysmography Measurement
把血流动力学的 Navier-Stokes 方程离散解推导为因果卷积，为 rPPG 的时序建模提供物理依据；其零 FLOPs 的轴向通道交换与逐帧自适应空间掩码，可直接迁移到其他需要跨区域交互与噪声抑制的面部视频时序任务。
原文依据 the pulse signal follows a second-order dynamical system whose discrete solution naturally leads to a causal convolution
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论远程光电容积脉搏波（rPPG）测量可实现非接触式生理监测，但在头部运动与光照变化下会出现精度下降。现有深度学习方法大多依赖启发式设计、缺乏理论依据，限制了鲁棒性与可解释性。本文从血液动力学的 Navier-Stokes 方程出发，提出一种物理信息驱动的 rPPG 范式，证明脉搏信号服从二阶动力系统，其离散解自然导出一个因果卷积，从而为使用时序卷积网络（TCN）提供了依据。基于该原理，我们设计了 PHASE-Net，一个包含三个关键组件的轻量模型：1）零 FLOPs 的轴向交换模块，通过交换或转置少量空间通道来混合相距较远的面部区域，在不改变时序顺序的前提下增强跨区域特征交互；2）自适应空间滤波器，为每一帧学习一个软空间掩码，以突出信号丰富区域并抑制噪声，从而得到更干净的特征图；3）门控 TCN，一种带门控的因果空洞 TCN，用于建模长程时序动态以实现准确的脉搏恢复。大量实验表明，PHASE-Net 取得了最优性能并具备出色的效率，提供了一个有理论依据且可直接部署的 rPPG 方案。
英文原文摘要 1337 字符
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, limiting 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, justifying the use of a Temporal Convolutional Network (TCN). Based on this principle, we design the PHASE-Net, a lightweight model with three key components: 1) Zero-FLOPs Axial Swapper module to swap or transpose a few spatial channels to mix distant facial regions, boosting cross-region feature interaction without changing temporal order; 2) Adaptive Spatial Filter to learn a soft spatial mask per frame to highlight signal-rich areas and suppress noise for cleaner feature maps; 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 and strong efficiency, offering a theoretically grounded and deployment-ready rPPG solution.