PhysFlow: Frequency Decoupled with Dual-Field Rectified Flow for Remote Photoplethysmography
Remote Photoplethysmography (rPPG) enables contactless pulse estimation from facial videos, serving as a vital tool for health monitoring. However, current deep learning methods often struggle under complex disturbances, particularly varying illumination, facial expressions, and unconstrained head movements. In such scenarios, subtle physiological signals are easily dominated by external interference, making the recovered rPPG waveform unstable and unreliable. One important reason is that most existing methods directly model the rPPG signal in a unified manner, where different signal components are coupled during reconstruction. This makes it difficult to preserve weak pulse-related varia...
| Authors | Zixu Li, jianjun Qian, Hang Shao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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