Rhythm-Structured Predictive Learning for Remote Photoplethysmography

• URL: http://arxiv.org/abs/2606.31736v1
• Date: Tue, 30 Jun 2026 14:32:53 GMT
• Title: Rhythm-Structured Predictive Learning for Remote Photoplethysmography
• Abstract summary: RhythmJEPA predicts latent teacher representations from masked facial videos.<n>CRSP estimates frame-wise latent physiological states and decodes the most plausible cyclic state path.<n>DOM captures both local temporal continuity and long-range rhythm-consistent dependencies.<n>SPM extracts compact pulse-sensitive facial tokens with a favorable balance between complexity and performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Remote photoplethysmography (rPPG) estimates physiological signals from facial videos by analyzing subtle pulse induced skin color variations. Despite recent progress, existing self-supervised rPPG methods mainly reconstruct masked pixels or low-level visual representations, which can bias the model toward facial appearance rather than latent physiological dy namics. Moreover, most recent Mamba-based approaches scan facial video tokens only in chronological order, limiting their ability to exploit the cyclic structure of pulse signals. To ad dress these limitations, we propose RhythmJEPA, a rhythm structured joint-embedding predictive learning framework for rPPG. Instead of reconstructing RGB frames, RhythmJEPA predicts latent teacher representations from masked facial videos, thereby encouraging physiology-aware representation learning in the embedding space. To explicitly model pulse-related tem poral structure, we introduce a Cyclic Rhythm-State Plan ner (CRSP), which estimates frame-wise latent physiological states and decodes the most plausible cyclic state path via dynamic programming with a constrained transition grammar. Guided by the decoded states, we further design a Dual Order Mamba Encoder (DOM), which combines conventional chronological scanning with state-ordered scanning to capture both local temporal continuity and long-range rhythm-consistent dependencies. Finally, a lightweight Spatial Pulse Mixer (SPM) extracts compact pulse-sensitive facial tokens with a favorable balance between complexity and performance. Experiments on PURE, UBFC-rPPG, and MMPD show competitive performance over representative rPPG methods. The codes are available at https://github.com/deconasser/RhythmJEPA.

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