arXiv 2509.248502026-03-07cs.CV

PHASE-Net：基于物理的谐波注意力系统用于高效的远程光体积脉搏波测记测量

PHASE-Net: Physics-Grounded Harmonic Attention System for Efficient Remote Photoplethysmography Measurement

Great Bay University（大湾大学）；Hefei University of Technology（合肥工业大学）；Macao Polytechnic University（澳门 polytechnic 大学）；University of Science and Technology Beijing（北京科技大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳分校）；Dongguan Key Laboratory for Intelligence and Information Technology（东莞智能与信息科技重点实验室）

Bo Zhao, Dan Guo, Junzhe Cao, Yong Xu, Bochao Zou, Tao Tan, Yue Sun, Zitong Yu

备注：: Accepted by CVPR2026

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AI总结：

PHASE-Net基于物理原理设计，通过谐波注意力系统实现高效的远程光体积脉搏波测记测量，提升鲁棒性和可解释性。

摘要：

远程光体积脉搏波测记（rPPG）测量能够实现非接触式生理监测，但在头部运动和光照变化下会出现准确性下降的问题。现有的深度学习方法大多是启发式的，缺乏理论基础，限制了鲁棒性和可解释性。在本文中，我们提出了一种基于血流动力学纳维-斯托克斯方程的rPPG范式，证明脉搏信号遵循一个二阶动力系统，其离散解自然导致因果卷积，从而证明了使用时间卷积网络（TCN）的合理性。基于这一原理，我们设计了PHASE-Net，一个轻量级模型，包含三个关键组件：1）零FLOPs轴向交换模块，用于交换或转置少量空间通道以混合远距离面部区域，提升跨区域特征交互而不改变时间顺序；2）自适应空间滤波器，用于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获得更干净的特征图；3）门控TCN，一种具有门控的因果扩张TCN，用于建模长距离时间动态以实现准确的脉搏恢复。大量实验表明，PHASE-Net在性能和效率方面均达到最先进的水平，提供了一种理论上有依据且可部署的rPPG解决方案。源代码可在https://github.com/Alex036225/PhaseNet上获得。

英文摘要：

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. The source code is available at https://github.com/Alex036225/PhaseNet.

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