# Non-Contrastive Unsupervised Learning of Physiological Signals from Video

arXiv: https://arxiv.org/abs/2303.07944 · HTML: https://mlsanity.com/paper/2303.07944/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 60 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 28 of 28)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Whitening for Self-Supervised Representation Learning](https://mlsanity.com/paper/2007.06346/) (2020)
- [CelebV-HQ: A Large-Scale Video Facial Attributes Dataset](https://mlsanity.com/paper/2207.12393/) (2022)
- [DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks](https://mlsanity.com/paper/1805.07888/) (2018)
- [Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement](https://mlsanity.com/paper/2006.03790/) (2020)
- [Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks](https://mlsanity.com/paper/1905.02419/) (2019)
- [PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer](https://mlsanity.com/paper/2111.12082/) (2021)
- [RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation](https://mlsanity.com/paper/1910.11515/) (2019)
- [The Way to my Heart is through Contrastive Learning: Remote Photoplethysmography from Unlabelled Video](https://mlsanity.com/paper/2111.09748/) (2021)
- [PulseGAN: Learning to generate realistic pulse waveforms in remote photoplethysmography](https://mlsanity.com/paper/2006.02699/) (2020)
- [VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video](https://mlsanity.com/paper/1810.04927/) (2018)
- [Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner](https://mlsanity.com/paper/2007.06786/) (2020)
- [Camera Measurement of Physiological Vital Signs](https://mlsanity.com/paper/2111.11547/) (2021)
- [Contrast-Phys: Unsupervised Video-based Remote Physiological Measurement via Spatiotemporal Contrast](https://mlsanity.com/paper/2208.04378/) (2022)
- [Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks](https://mlsanity.com/paper/1611.05916/) (2016)
- [SimPer: Simple Self-Supervised Learning of Periodic Targets](https://mlsanity.com/paper/2210.03115/) (2022)
- [SCAMPS: Synthetics for Camera Measurement of Physiological Signals](https://mlsanity.com/paper/2206.04197/) (2022)
- [Self-supervised Representation Learning Framework for Remote Physiological Measurement Using Spatiotemporal Augmentation Loss](https://mlsanity.com/paper/2107.07695/) (2021)
- [Analysis of CNN-based remote-PPG to understand limitations and sensitivities](https://mlsanity.com/paper/1911.02736/) (2019)
- [Remote Photoplethysmography: Rarely Considered Factors](https://mlsanity.com/paper/2004.12695/) (2020)

## Built on by (most cited first, 30 of 37)

- [rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement](https://mlsanity.com/paper/2306.02301/) (2023)
- [Contrast-Phys+: Unsupervised and Weakly-supervised Video-based Remote Physiological Measurement via Spatiotemporal Contrast](https://mlsanity.com/paper/2309.06924/) (2023)
- [RhythmFormer: Extracting Patterned rPPG Signals based on Periodic Sparse Attention](https://mlsanity.com/paper/2402.12788/) (2024)
- [Advancing Generalizable Remote Physiological Measurement through the Integration of Explicit and Implicit Prior Knowledge](https://mlsanity.com/paper/2403.06947/) (2024)
- [Motion Matters: Neural Motion Transfer for Better Camera Physiological Measurement](https://mlsanity.com/paper/2303.12059/) (2023)
- [egoPPG: Heart Rate Estimation from Eye-Tracking Cameras in Egocentric Systems to Benefit Downstream Vision Tasks](https://mlsanity.com/paper/2502.20879/) (2025)
- [PhysDrive: A Multimodal Remote Physiological Measurement Dataset for In-vehicle Driver Monitoring](https://mlsanity.com/paper/2507.19172/) (2025)
- [Full-Body Cardiovascular Sensing with Remote Photoplethysmography](https://mlsanity.com/paper/2303.09638/) (2023)
- [An Unsupervised Approach for Periodic Source Detection in Time Series](https://mlsanity.com/paper/2406.00566/) (2024)
- [FreqPhys: Repurposing Implicit Physiological Frequency Prior for Robust Remote Photoplethysmography](https://mlsanity.com/paper/2604.00534/) (2026)
- [SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers](https://mlsanity.com/paper/2404.03831/) (2024)
- [Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin Tones](https://mlsanity.com/paper/2404.05003/) (2024)
- [DD-rPPGNet: De-interfering and Descriptive Feature Learning for Unsupervised rPPG Estimation](https://mlsanity.com/paper/2407.21402/) (2024)
- [Semi-rPPG: Semi-Supervised Remote Physiological Measurement with Curriculum Pseudo-Labeling](https://mlsanity.com/paper/2502.03855/) (2025)
- [SiNC+: Adaptive Camera-Based Vitals with Unsupervised Learning of Periodic Signals](https://mlsanity.com/paper/2404.13449/) (2024)
- [Evaluation of Video-Based rPPG in Challenging Environments: Artifact Mitigation and Network Resilience](https://mlsanity.com/paper/2405.01230/) (2024)
- [Continual Learning for Remote Physiological Measurement: Minimize Forgetting and Simplify Inference](https://mlsanity.com/paper/2407.13974/) (2024)
- [rPPG-SysDiaGAN: Systolic-Diastolic Feature Localization in rPPG Using Generative Adversarial Network with Multi-Domain Discriminator](https://mlsanity.com/paper/2504.01220/) (2025)
- [BeatFormer: Efficient motion-robust remote heart rate estimation through unsupervised spectral zoomed attention filters](https://mlsanity.com/paper/2507.14885/) (2025)
- [MVRD-Bench: Multi-View Learning and Benchmarking for Dynamic Remote Photoplethysmography under Occlusion](https://mlsanity.com/paper/2603.22826/) (2026)
- [Joint Spatial-Temporal Modeling and Contrastive Learning for Self-supervised Heart Rate Measurement](https://mlsanity.com/paper/2406.04942/) (2024)
- [Periodic-MAE: Periodic Video Masked Autoencoder for rPPG Estimation](https://mlsanity.com/paper/2506.21855/) (2025)
- [rPPG-VQA: A Video Quality Assessment Framework for Unsupervised rPPG Training](https://mlsanity.com/paper/2604.11156/) (2026)
- [Intervention-Based Self-Supervised Learning: A Causal Probe Paradigm for Remote Photoplethysmography](https://mlsanity.com/paper/2605.00882/) (2026)
- [RPM-Distill: Physiology-guided Adaptive Cross-modal Distillation for Robust Remote Physiological Measurement](https://mlsanity.com/paper/2606.28089/) (2026)
- [Biometric Authentication Based on Enhanced Remote Photoplethysmography Signal Morphology](https://mlsanity.com/paper/2407.04127/) (2024)
- [Bootstrapping Vision-language Models for Self-supervised Remote Physiological Measurement](https://mlsanity.com/paper/2407.08507/) (2024)
- [Ultra-Short rPPG Estimation via Periodicity Guidance and Signal Reconstruction](https://mlsanity.com/paper/2506.22078/) (2025)
- [FedHUG: Federated Heterogeneous Unsupervised Generalization for Remote Physiological Measurements](https://mlsanity.com/paper/2510.12132/) (2025)
- [Radar-APLANC: Unsupervised Radar-based Heartbeat Sensing via Augmented Pseudo-Label and Noise Contrast](https://mlsanity.com/paper/2511.08071/) (2025)

## Abstract (arXiv)

Subtle periodic signals such as blood volume pulse and respiration can be extracted from RGB video, enabling remote health monitoring at low cost. Advancements in remote pulse estimation -- or remote photoplethysmography (rPPG) -- are currently driven by deep learning solutions. However, modern approaches are trained and evaluated on benchmark datasets with associated ground truth from contact-PPG sensors. We present the first non-contrastive unsupervised learning framework for signal regression to break free from the constraints of labelled video data. With minimal assumptions of periodicity and finite bandwidth, our approach is capable of discovering the blood volume pulse directly from unlabelled videos. We find that encouraging sparse power spectra within normal physiological bandlimits and variance over batches of power spectra is sufficient for learning visual features of periodic signals. We perform the first experiments utilizing unlabelled video data not specifically created for rPPG to train robust pulse rate estimators. Given the limited inductive biases and impressive empirical results, the approach is theoretically capable of discovering other periodic signals from video, enabling multiple physiological measurements without the need for ground truth signals. Codes to fully reproduce the experiments are made available along with the paper.