This report introduces VitalLens 2.0, a new deep learning model for estimating physiological signals from face video. This new model demonstrates a significant leap in accuracy for remote photoplethysmography (rPPG), enabling the robust estimation of not only heart rate (HR) and respiratory rate (RR) but also Heart Rate Variability (HRV) metrics. This advance is achieved through a combination of a new model architecture and a substantial increase in the size and diversity of our training data, now totaling 1,413 unique individuals. We evaluate VitalLens 2.0 on a new, combined test set of 422 unique individuals from four public and private datasets. When averaging results by individual, VitalLens 2.0 achieves a Mean Absolute Error (MAE) of 1.57 bpm for HR, 1.08 bpm for RR, 10.18 ms for HRV-SDNN, and 16.45 ms for HRV-RMSSD. These results represent a new state-of-the-art, significantly outperforming previous methods. This model is now available to developers via the VitalLens API at https://rouast.com/api.
@article{arxiv.2510.27028,
title = {VitalLens 2.0: High-Fidelity rPPG for Heart Rate Variability Estimation from Face Video},
author = {Philipp V. Rouast},
journal= {arXiv preprint arXiv:2510.27028},
year = {2025}
}
Comments
Technical Report. 8 pages, 5 figures. Introduces the VitalLens 2.0 model for rPPG and Heart Rate Variability (HRV) estimation. Project website: https://rouast.com/api