{"ID":5443780,"CreatedAt":"2026-07-01T02:07:11.383974684Z","UpdatedAt":"2026-07-03T14:25:27.813080916Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31736","arxiv_id":"2606.31736","title":"Rhythm-Structured Predictive Learning for Remote Photoplethysmography","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.","short_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 appea...","url_abs":"https://arxiv.org/abs/2606.31736","url_pdf":"https://arxiv.org/pdf/2606.31736v1","authors":"[\"Ba-Thinh Nguyen\",\"Huu-Dung Nguyen\",\"Thi-Duyen Ngo\",\"Thanh-Ha Le\"]","published":"2026-06-30T14:32:53Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":613804,"CreatedAt":"2026-07-01T02:07:11.383974684Z","UpdatedAt":"2026-07-01T02:07:11.383974684Z","DeletedAt":null,"paper_id":5443780,"paper_url":"https://arxiv.org/abs/2606.31736","paper_title":"Rhythm-Structured Predictive Learning for Remote Photoplethysmography","repo_url":"https://github.com/deconasser/RhythmJEPA","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}