FreqPhys: 重新利用隐含的生理频率先验以实现鲁棒的远程光体积脉搏波测记
FreqPhys: Repurposing Implicit Physiological Frequency Prior for Robust Remote Photoplethysmography
AI总结：
本文提出FreqPhys框架，通过显式利用生理频率先验提升远程光体积脉搏波测记的鲁棒性，采用频域引导方法优化信号恢复，实验表明其在运动干扰下性能显著提升。
摘要：
远程光体积脉搏波测记（rPPG）通过面部视频捕捉细微的皮肤颜色变化实现无接触生理监测。然而，现有方法主要依赖时域建模，易受运动伪影和光照波动影响，弱生理信号易被噪声淹没。为解决这些问题，我们提出FreqPhys，一种频率引导的rPPG框架，显式利用生理频率先验进行鲁棒信号恢复。具体而言，FreqPhys首先应用生理带通滤波模块抑制带外干扰，然后进行生理频谱调制与自适应频谱选择，以强调脉搏相关频率成分并抑制残余带内噪声。跨域表示学习模块进一步融合这些频谱先验与深度时域特征，以捕捉信息丰富的空间-时间依赖性。最后，频率感知的条件扩散过程逐步重建高保真的rPPG信号。在六个基准数据集上的广泛实验表明，FreqPhys在状态-of-the-art方法上取得了显著改进，特别是在挑战性的运动条件下。它突显了显式建模生理频率先验的重要性。源代码将被发布。
英文摘要：
Remote photoplethysmography (rPPG) enables contactless physiological monitoring by capturing subtle skin-color variations from facial videos. However, most existing methods predominantly rely on time-domain modeling, making them vulnerable to motion artifacts and illumination fluctuations, where weak physiological clues are easily overwhelmed by noise. To address these challenges, we propose FreqPhys, a frequency-guided rPPG framework that explicitly leverages physiological frequency priors for robust signal recovery. Specifically, FreqPhys first applies a Physiological Bandpass Filtering module to suppress out-of-band interference, and then performs Physiological Spectrum Modulation together with adaptive spectral selection to emphasize pulse-related frequency components while suppress residual in-band noise. A Cross-domain Representation Learning module further fuses these spectral priors with deep time-domain features to capture informative spatial--temporal dependencies. Finally, a frequency-aware conditional diffusion process progressively reconstructs high-fidelity rPPG signals. Extensive experiments on six benchmarks demonstrate that FreqPhys yields significant improvements over state-of-the-art approaches, particularly under challenging motion conditions. It highlights the importance of explicitly modeling physiological frequency priors. The source code will be released.
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。
