FreqPhys: Repurposing Implicit Physiological Frequency Prior for Robust Remote Photoplethysmography

Remote photoplethysmography (rPPG) enables contactlessphysiological monitoring by capturing subtle skin-color variations fromfacial videos. However, most existing methods predominantly rely ontime-domain modeling, making them vulnerable to motion artifacts andillumination fluctuations, where weak physiological clues are easily over-whelmed by noise. To address these challenges, we propose FreqPhys,a frequency-guided rPPG framework that explicitly leverages physio-logical frequency priors for robust signal recovery. Specifically, Freq-Phys first applies a Physiological Bandpass Filtering module to sup-press out-of-band interference, and then performs Physiological Spec-trum Modulation together with adaptive spectral selection to empha-size pulse-related frequency components while suppress residual in-bandnoise. A Cross-domain Representation Learning module further fusesthese spectral priors with deep time-domain features to capture infor-mative spatial–temporal dependencies. Finally, a frequency-aware con-ditional diffusion process progressively reconstructs high-fidelity rPPGsignals. Extensive experiments on six benchmarks demonstrate that Fre-qPhys yields significant improvements over state-of-the-art approaches,particularly under challenging motion conditions. It highlights the im-portance of explicitly modeling physiological frequency priors. Our codeis available at the https://github.com/WeiQian98/FreqPhys.