FLOW: Optimal Transport-Driven Feature Warping for Generalized Remote Physiological Measurement

Remote photoplethysmography (rPPG) enables non-contact physiological measurement from facial videos but often suffers from severe performance degradation under domain shifts. Traditional STMap-based methods [??] rely on predefined spatio-temporal representations that offer engineered robustness but discard fine-grained temporal cues. In contrast, end-to-end rPPG models directly learn hierarchical features from raw videos, capturing richer physiological patterns yet remaining highly sensitive to variations in illumination, motion, and camera sensors. To address these challenges, we propose FLOW (Feature-Level Optimal Warping), an Optimal Transport (OT)-driven and plug-and-play framework for multi-source domain generalization in rPPG measurement.FLOW formulates domain shifts as structured Optimal Transport problems and performs feature-level warping to align multiple source domains in a shared latent space. Specifically, a dual-consistency regularization is proposed to enforce both frequency fidelity and mapping invariance, while a shape-adaptive alignment module is designed to enable architecture-agnostic integration without re-training. We further derive a generalization bound based on conditional OT discrepancy, providing theoretical insight into FLOW's robustness under distributional shifts. Extensive experiments across diverse rPPG benchmarks demonstrate that FLOW consistently improves cross-domain generalization while maintaining lightweight and modular deployment.