Reading the Pulse Across Scales: Adaptive Wavelet Decomposition and Interaction

Remote photoplethysmography (rPPG) recovers physiological signals from facial videos, where subtle pulse responses are entangled with motion, illumination variations, and imaging noise. These mixed dynamics evolve over different temporal scales and vary across recordings. Existing methods primarily learn from mixed spatiotemporal representations, while predefined wavelet bases offer limited flexibility to accommodate diverse physiological rhythms and interference patterns. To read the pulse across scales, we propose PhysWave, a framework that couples adaptive wavelet decomposition with hierarchical component interaction. PhysWave adaptively combines multiple learnable wavelet experts at each decomposition level to organize local video dynamics into components at different temporal scales. Since temporal decomposition alone does not isolate physiological information, we model intra-component dependencies through sparse attention with a shared, orthogonally initialized projection and learnable gating to refine query–key matching. Temporal spectra then guide the exchange of spatiotemporal features across components, integrating complementary cues across scales for waveform estimation. Experiments on five public rPPG benchmarks demonstrate competitive performance under intra-dataset and cross-dataset evaluation, while ablation studies support the effectiveness of adaptive decomposition and component interaction. The code is available at the \url{https://anonymous.4open.science/r/JSNISE-BEBB.