RPM-Distill: Physiology-guided Adaptive Cross-modal Distillation for Robust Remote Physiological Measurement

Video-based remote physiological measurement (RPM) ishighly accessible but remains fragile under varying illumination, skintones, and motion. Radio frequency (RF) radar is largely invariant to il-lumination and appearance, providing complementary cardio-respiratorymicro-motion cues; however, requiring radar at inference is often imprac-tical due to its limited ubiquity and deployment overhead. We proposeRPM-Distill, a physiology-guided cross-modal distillation frameworkthat leverages synchronized radar only during training while retainingvideo-only inference. Our key observation is that although RGB and RFwaveforms differ in sensing physics and time-domain morphology, theyshare similar latent periodic rhythm in the frequency domain. We thusdistill physiology-structured spectral evidence to improve robustness, vialosses that (i) anchor the fundamental peak, (ii) match the off-peak back-ground distribution, and (iii) preserve spectral morphology and sharp-ness. To avoid negative transfer under sample-level teacher quality andalignment uncertainty, a spectral policy network predicts sample-leveldistillation gates and component weights from the student–teacher spec-tral relation map, learned with a meta bilevel objective on a small labeledvalidation split. Through extensive experiments in challenging conditionsand cross-dataset settings, RPM-Distill reduces MAE by up to 81% andimproves correlation by up to 21% over the best video-only baseline.Code is at https://github.com/WJULYW/RPM-Distill.