# Learning Motion-Robust Remote Photoplethysmography through Arbitrary Resolution Videos

arXiv: https://arxiv.org/abs/2211.16922 · HTML: https://mlsanity.com/paper/2211.16922/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 55 references that are in the map.
- First posted on arXiv 30 Nov 2022.

## Builds on (most cited first, 22 of 22)

- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [Learning Continuous Image Representation with Local Implicit Image Function](https://mlsanity.com/paper/2012.09161/) (2020)
- [Optical Flow Estimation using a Spatial Pyramid Network](https://mlsanity.com/paper/1611.00850/) (2016)
- [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment](https://mlsanity.com/paper/2104.13371/) (2021)
- [Meta-SR: A Magnification-Arbitrary Network for Super-Resolution](https://mlsanity.com/paper/1903.00875/) (2019)
- [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)
- [Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling](https://mlsanity.com/paper/2007.08213/) (2020)
- [The Way to my Heart is through Contrastive Learning: Remote Photoplethysmography from Unlabelled Video](https://mlsanity.com/paper/2111.09748/) (2021)
- [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)
- [A Reproducible Study on Remote Heart Rate Measurement](https://mlsanity.com/paper/1709.00962/) (2017)
- [Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning](https://mlsanity.com/paper/2203.06541/) (2022)
- [TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection](https://mlsanity.com/paper/2104.07419/) (2021)
- [AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching](https://mlsanity.com/paper/2004.12292/) (2020)
- [Analysis of CNN-based remote-PPG to understand limitations and sensitivities](https://mlsanity.com/paper/1911.02736/) (2019)
- [Robust Face Alignment by Multi-order High-precision Hourglass Network](https://mlsanity.com/paper/2010.08722/) (2020)
- [Efficient Remote Photoplethysmography with Temporal Derivative Modules and Time-Shift Invariant Loss](https://mlsanity.com/paper/2203.10882/) (2022)
- [Remote Photoplethysmography: Rarely Considered Factors](https://mlsanity.com/paper/2004.12695/) (2020)

## Built on by (most cited first, 26 of 26)

- [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)
- [RhythmMamba: Fast, Lightweight, and Accurate Remote Physiological Measurement](https://mlsanity.com/paper/2404.06483/) (2024)
- [Spiking-PhysFormer: Camera-Based Remote Photoplethysmography with Parallel Spike-driven Transformer](https://mlsanity.com/paper/2402.04798/) (2024)
- [Dual-path TokenLearner for Remote Photoplethysmography-based Physiological Measurement with Facial Videos](https://mlsanity.com/paper/2308.07771/) (2023)
- [PhysMLE: Generalizable and Priors-Inclusive Multi-task Remote Physiological Measurement](https://mlsanity.com/paper/2405.06201/) (2024)
- [Orientation-conditioned Facial Texture Mapping for Video-based Facial Remote Photoplethysmography Estimation](https://mlsanity.com/paper/2404.09378/) (2024)
- [Remote Photoplethysmography in Real-World and Extreme Lighting Scenarios](https://mlsanity.com/paper/2503.11465/) (2025)
- [Toward Motion Robustness: A masked attention regularization framework in remote photoplethysmography](https://mlsanity.com/paper/2407.06653/) (2024)
- [PhysMamba: State Space Duality Model for Remote Physiological Measurement](https://mlsanity.com/paper/2408.01077/) (2024)
- [UbiHR: Resource-efficient Long-range Heart Rate Sensing on Ubiquitous Devices](https://mlsanity.com/paper/2410.19279/) (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)
- [Remote Bio-Sensing: Open Source Benchmark Framework for Fair Evaluation of rPPG](https://mlsanity.com/paper/2307.12644/) (2023)
- [Remote Heart Rate Monitoring in Smart Environments from Videos with Self-supervised Pre-training](https://mlsanity.com/paper/2310.15388/) (2023)
- [Periodic-MAE: Periodic Video Masked Autoencoder for rPPG Estimation](https://mlsanity.com/paper/2506.21855/) (2025)
- [PhysFlow: Frequency Decoupled with Dual-Field Rectified Flow for Remote Photoplethysmography](https://mlsanity.com/paper/2606.23226/) (2026)
- [RPM-Distill: Physiology-guided Adaptive Cross-modal Distillation for Robust Remote Physiological Measurement](https://mlsanity.com/paper/2606.28089/) (2026)
- [PhysAgent: A Multi-Agent Framework for Reliable Remote Heart Rate Estimation](https://mlsanity.com/paper/2608.00066/) (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)
- [Facial Spatiotemporal Graphs: Leveraging the 3D Facial Surface for Remote Physiological Measurement](https://mlsanity.com/paper/2601.13724/) (2026)
- [LQ-rPPG: A Label-Quantized Coarse-to-Fine Learning Framework for Remote Physiological Measurement](https://mlsanity.com/paper/2605.23174/) (2026)
- [StreamPPG: Low-Latency rPPG Estimation via Consistent Privileged Learning](https://mlsanity.com/paper/2606.23186/) (2026)
- [PhysVR: Vision-Language Model Guided Interference-aware Temporal Feature Refinement for Remote Physiological Measurement](https://mlsanity.com/paper/2608.29663/) (2026)

## Abstract (arXiv)

Remote photoplethysmography (rPPG) enables non-contact heart rate (HR) estimation from facial videos which gives significant convenience compared with traditional contact-based measurements. In the real-world long-term health monitoring scenario, the distance of the participants and their head movements usually vary by time, resulting in the inaccurate rPPG measurement due to the varying face resolution and complex motion artifacts. Different from the previous rPPG models designed for a constant distance between camera and participants, in this paper, we propose two plug-and-play blocks (i.e., physiological signal feature extraction block (PFE) and temporal face alignment block (TFA)) to alleviate the degradation of changing distance and head motion. On one side, guided with representative-area information, PFE adaptively encodes the arbitrary resolution facial frames to the fixed-resolution facial structure features. On the other side, leveraging the estimated optical flow, TFA is able to counteract the rPPG signal confusion caused by the head movement thus benefit the motion-robust rPPG signal recovery. Besides, we also train the model with a cross-resolution constraint using a two-stream dual-resolution framework, which further helps PFE learn resolution-robust facial rPPG features. Extensive experiments on three benchmark datasets (UBFC-rPPG, COHFACE and PURE) demonstrate the superior performance of the proposed method. One highlight is that with PFE and TFA, the off-the-shelf spatio-temporal rPPG models can predict more robust rPPG signals under both varying face resolution and severe head movement scenarios. The codes are available at https://github.com/LJW-GIT/Arbitrary_Resolution_rPPG.