[2605.00882] Intervention-Based Self-Supervised Learning: A Causal Probe Paradigm for Remote Photoplethysmography
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.00882 (cs)
[Submitted on 26 Apr 2026]
Title:Intervention-Based Self-Supervised Learning: A Causal Probe Paradigm for Remote Photoplethysmography
Authors:Zhiyi Niu, Xiaoguang Tu, Bo Zhao, Junzhe Cao, Dan Guo, Zitong Yu
View a PDF of the paper titled Intervention-Based Self-Supervised Learning: A Causal Probe Paradigm for Remote Photoplethysmography, by Zhiyi Niu and 5 other authors
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Abstract:Remote Photoplethysmography (rPPG) enables convenient non-contact physiological measurement. Existing Self-Supervised Learning (SSL) methods commonly fall into a correlation trap: they tend to learn the most dominant periodic signals in the data, such as high-energy motion or illumination noise, rather than the faint, true rPPG signal, leading to poor model generalization. To address this, we propose a new SSL paradigm, Physiological Causal Probing (PCP), which treats the latent rPPG signal as the underlying physical source and the resulting pixel chrominance variations as its visual manifestation. Its core idea is to shift from passive correlation learning to active, precise intervention: it intervenes on the video based on a proposed rPPG hypothesis, and verifies whether the post-intervention changes match physical expectations. We propose the Interv-rPPG framework to implement PCP: an rPPG extractor named PhysMambaFormer hypothesizes the rPPG signal, while a Controllable Physiological Signal Editor conducts precise chrominance-domain interventions on videos based on this hypothesis. Interv-rPPG validates the physical realism of the hypothesis through Falsifiability via Nulling' and Axiomatic Equivariance'. Our editor achieves precise editing of the rPPG signal by intervening in the low-frequency chrominance components of the video. Our method improves both in-domain and cross-domain performance on challenging datasets such as VIPL-HR and MMPD. Furthermore, it surpasses the supervised baseline in complex cross-dataset settings, while remaining competitive on clean datasets where the intervention mechanism may introduce slight residual chrominance noise. Extensive experiments, including diagnostic analysis of nuisance sensitivity, demonstrate that the PCP paradigm effectively resists motion and illumination artifacts.
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Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.00882 [cs.CV]
(or arXiv:2605.00882v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.00882
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arXiv-issued DOI via DataCite
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From: Zhiyi Niu [view email] [v1] Sun, 26 Apr 2026 09:56:09 UTC (2,097 KB)
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