FCUS-rPPG: A Fast-Converging Unsupervised Framework for Remote Photoplethysmography via Gradient Oscillation Suppression
Quick Take
FCUS-rPPG is a novel unsupervised framework for remote photoplethysmography that converges rapidly, requiring only one training epoch compared to existing methods that need tens to hundreds. It employs a unique optimization strategy that enhances generalization and stability, achieving state-of-the-art performance across five datasets.
Key Points
- FCUS-rPPG requires only one training epoch for effective BVP signal extraction.
- The framework achieves state-of-the-art performance in cross-dataset evaluations.
- It employs a post-verification masking mechanism to filter misleading gradients.
- A perturbation-based strategy smooths the loss landscape for better generalization.
- Source code will be publicly available on GitHub.
Article Content
From source RSS / original summaryarXiv:2606. 03050v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) enables non-contact extraction of blood volume pulse (BVP) signals using consumer-grade cameras. Recent unsupervised rPPG methods learn BVP representations without requiring ground-truth physiological annotations, yet their optimization is often hindered by noisy and unstable gradients, resulting in slow convergence and limited cross-domain generalization.
In this paper, we propose FCUS-rPPG, a fast-converging unsupervised rPPG framework with strong generalization capability. Motivated by the observation that BVP representations exhibit both multi-spectral covariation and low-dimensional manifold structure, we design a spectrally shared backbone that facilitates BVP feature disentanglement while improving optimization efficiency.
To jointly enhance convergence stability and generalization performance, we further develop a unified optimization framework operating at the gradient, loss-landscape, and feature-representation levels.
Specifically, a post-verification masking mechanism filters out misleading gradients according to the weak-amplitude physiological prior of BVP signals; a perturbation-based loss landscape smoothing strategy steers optimization toward more generalizable flat minima; and a noise-aware null-space regularization constrains feature updates to the orthogonal complement of the noise subspace, thereby mitigating noise-induced representation drift.
Extensive experiments on five datasets demonstrate that FCUS-rPPG requires only one training epoch, whereas existing methods typically require tens to hundreds of epochs. Notably, FCUS-rPPG consistently achieves state-of-the-art (SOTA) performance in cross-dataset evaluations. This study provides an efficient and robust solution to the real-world deployment of unsupervised rPPG. The source code will be publicly available at https://github. com/JiaJieLee/FCUS-rPPG.
Reader Mode unavailable (could not extract clean content).
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CV
See more →Plan2Map: A Multimodal Benchmark for Document-Grounded Geospatial Boundary Reconstruction from Planning Records
Plan2Map introduces a 208-case benchmark for reconstructing geospatial boundaries from UK planning documents. The GeoPlanAgent system achieves a mean IoU of 0.736, significantly outperforming baseline models, highlighting the challenges in localization and map registration.