FreqPhys: Repurposing Implicit Physiological Frequency Prior for Robust Remote Photoplethysmography

• URL: http://arxiv.org/abs/2604.00534v1
• Date: Wed, 01 Apr 2026 06:25:42 GMT
• Title: FreqPhys: Repurposing Implicit Physiological Frequency Prior for Robust Remote Photoplethysmography
• Abstract summary: Photoplethysmography enables contactless physiological monitoring by capturing subtle skin-color variations from videos.<n>FreqPhys is a frequency-guided rulation framework that explicitly leverages physiological frequency priors for robust signal recovery.<n>It yields significant improvements over state-of-the-art approaches, particularly under challenging motion conditions.
• Score: 54.32412212867472
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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