# Advancing Non-Contact Vital Sign Measurement using Synthetic Avatars

arXiv: https://arxiv.org/abs/2010.12949 · HTML: https://mlsanity.com/paper/2010.12949/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Vitals Measurement](https://mlsanity.com/paper/2110.04447/) (2021)
- [rPPG-Toolbox: Deep Remote PPG Toolbox](https://mlsanity.com/paper/2210.00716/) (2022)
- [Camera Measurement of Physiological Vital Signs](https://mlsanity.com/paper/2111.11547/) (2021)
- [SCAMPS: Synthetics for Camera Measurement of Physiological Signals](https://mlsanity.com/paper/2206.04197/) (2022)
- [Dual-path TokenLearner for Remote Photoplethysmography-based Physiological Measurement with Facial Videos](https://mlsanity.com/paper/2308.07771/) (2023)
- [egoPPG: Heart Rate Estimation from Eye-Tracking Cameras in Egocentric Systems to Benefit Downstream Vision Tasks](https://mlsanity.com/paper/2502.20879/) (2025)
- [Synthetic Data for Multi-Parameter Camera-Based Physiological Sensing](https://mlsanity.com/paper/2110.04902/) (2021)
- [Synthetic Data in Healthcare](https://mlsanity.com/paper/2304.03243/) (2023)
- [Algorithmic Principles of Camera-based Respiratory Motion Extraction](https://mlsanity.com/paper/2105.07537/) (2021)
- [Overcoming Difficulty in Obtaining Dark-skinned Subjects for Remote-PPG by Synthetic Augmentation](https://mlsanity.com/paper/2106.06007/) (2021)
- [MobilePhys: Personalized Mobile Camera-Based Contactless Physiological Sensing](https://mlsanity.com/paper/2201.04039/) (2022)
- [Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin Tones](https://mlsanity.com/paper/2404.05003/) (2024)
- [RGB Camera-based Physiological Sensing: Challenges and Future Directions](https://mlsanity.com/paper/2110.13362/) (2021)
- [PhySU-Net: Long Temporal Context Transformer for rPPG with Self-Supervised Pre-training](https://mlsanity.com/paper/2402.11913/) (2024)
- [Learning Higher-Order Dynamics in Video-Based Cardiac Measurement](https://mlsanity.com/paper/2110.03690/) (2021)

## Abstract (arXiv)

Non-contact physiological measurement has the potential to provide low-cost, non-invasive health monitoring. However, machine vision approaches are often limited by the availability and diversity of annotated video datasets resulting in poor generalization to complex real-life conditions. To address these challenges, this work proposes the use of synthetic avatars that display facial blood flow changes and allow for systematic generation of samples under a wide variety of conditions. Our results show that training on both simulated and real video data can lead to performance gains under challenging conditions. We show state-of-the-art performance on three large benchmark datasets and improved robustness to skin type and motion.