ResoPhys: Unsupervised Plug-and-Play Remote Physiological Measurement via Facial Videos of Arbitrary Resolution.

Abstract

Remote photoplethysmography (rPPG) is a non-contact method that detects blood volume changes in facial tissues from video. The non-invasiveness of rPPG makes it promising for applications in remote health monitoring and telemedicine. However, its real-world application is hindered by a fundamental challenge. Existing models are typically designed for high-resolution, fixed-size inputs, making them ill-suited for the arbitrary-resolution videos commonly encountered in practical scenarios due to dynamic camera-to-subject distances. To address this challenge, we propose ResoPhys, an unsupervised plug-and-play rPPG measurement method designed for facial videos of arbitrary resolution. This method first generates video pairs via random scaling and then employs specialized modules for arbitrary-resolution feature extraction and upsampling to analyze the resulting multi-scale features. The framework is optimized via an unsupervised contrastive learning approach using our proposed multi-resolution contrastive loss. To validate its performance across a spectrum of resolutions, we evaluated ResoPhys on several public datasets. The results demonstrate the superiority of our method over previous unsupervised approaches, exhibiting particular strength in challenging low-resolution scenarios, which underscores its robustness to resolution changes. Crucially, ResoPhys acts as a universal front-end that decouples resolution handling from signal extraction, empowering existing rPPG networks for effective deployment in arbitrary-resolution conditions. The source code is available at: https://github.com/HeZhongTian-xjtu/ResoPhys.