The paper introduces Heartian, a physiology‑aware framework that augments Gaussian head avatars with cardiac‑cycle‑dependent albedo modulation, enabling the encoding of remote photoplethysmography (rPPG) signals. By supervising with synchronized contact PPG, the method models the cardiac waveform as a sum of two Gaussians and learns per‑frame spatial residuals via a lightweight MLP. Experiments on 152 stationary recordings from UBFC‑rPPG, PURE, and MMPD show heart‑rate estimation errors as low as 0.29 bpm MAE and 0.38 % MAPE, while preserving reconstruction quality with negligible PSNR loss.
By Xiaoyue Fan, Jose Echevarria, Akshay Paruchuri, Kaan Ak\c{s}it
Gaussian head avatars typically model intrinsic facial appearance as temporally static, omitting subtle cardiac-induced skin-color variation. We propose $\unicode{x1F493}$Heartian, a physiology-aware...
arXiv:2606. 12378v1 Announce Type: cross Abstract: Physiological awareness is important for service, social, and assistive robots that interact with humans in everyday environments.
By Zhi Wei Xu, Torbj\"orn E. M. Nordling
Remote Photoplethysmography (rPPG) enables contactless pulse estimation from facial videos, serving as a vital tool for health monitoring. However, current deep learning methods often struggle under complex disturbances, particularly varying illumination, facial expressions, and unconstrained head movements.
The study examined how well physiological properties from contact photoplethysmography (PPG) can be recovered by camera-based remote photoplethysmography (rPPG) across 655 recordings. Using the CHROM method, the authors found that while heart‑rate estimates showed modest accuracy, other dynamic measures such as autocorrelation, spectral content, and Lyapunov exponents largely failed to preserve recording‑specific characteristics. They also observed that lighting and motion influence endpoint accuracy, yet these factors did not explain the lack of dynamical correspondence.
By Timothy Oladunni, Farouk Ganiyu-Adewumi
Remote photoplethysmography (rPPG) estimates the blood volume pulse (BVP) signal from facial videos, enabling contact-free health monitoring. Conventional clip-wise approaches, which use video clips as input, require capturing over one hundred frames before inference, thus introducing several seconds of delay and hindering real-time use.
The study examined how well specific physiological properties of contact photoplethysmography (PPG) can be recovered when converted to camera-derived remote photoplethysmography (rPPG) across 655 recordings. Using the CHROM method, the authors found that while overall heart‑rate accuracy was modest, many dynamic properties (e.g., autocorrelation, spectral measures, Lyapunov exponents) showed little recording‑specific correspondence, and differences varied with skin tone and observation conditions. The results demonstrate that preserving individual recording characteristics depends on the property and conditions, and population‑level agreement does not guarantee individual‑level fidelity.
VideoPulse is a neonatal dataset and end‑to‑end pipeline that estimates heart rate and peripheral capillary oxygen saturation (SpO2) from facial video without contact. The dataset contains 157 recordings from 52 neonates, and the pipeline uses face alignment, artifact‑aware supervision, and 3D CNN backbones to produce predictions every 2 seconds. On the NBHR dataset the model achieves a heart‑rate MAE of 2.97 bpm and SpO2 MAE of 1.69 %.
"whyItMatters":"The results show that short, unaligned neonatal video segments can accurately estimate vital signs, offering a low‑cost, non‑invasive monitoring option for neonatal intensive care."
By Deependra Dewagiri, Kamesh Anuradha, Pabadhi Liyanage, Helitha Kulatunga, Pamuditha Somarathne, Udaya S. K. P. Miriya Thanthrige, Nishani Lucas, Anusha Withana, Joshua P. Kulasingham
arXiv:2609.36607v1 Announce Type: new
Abstract: Remote photoplethysmography (rPPG) offers a promising non-contact solution for heart rate monitoring, yet its real-world robustness is fundamentally li...
By Jieying Wang, Xinqi Cai, Caifeng Shan, Wenjin Wang
arXiv:2607. 06598v1 Announce Type: cross Abstract: Heart rate measurement is one of the key requirements for real-time health monitoring, in particular for health caring of elderly people.
By Kelly Li, Fulu Li
The paper introduces DPNet, a Mamba-based deep learning framework for denoising photoplethysmography (PPG) signals while preserving physiological information. It incorporates a scale‑invariant signal‑to‑distortion ratio loss and an auxiliary heart‑rate predictor to enhance waveform fidelity and maintain heart‑rate accuracy. Experiments on the BIDMC dataset show that DPNet outperforms conventional filtering and existing neural models in robustness against synthetic noise and real‑world motion artifacts, making it suitable for wearable healthcare systems.
By I Chiu, Yu-Tung Liu, Kuan-Chen Wang, Hung-Yu Wei, Yu Tsao
arXiv:2608.28341v1 Announce Type: new
Abstract: Precise dense correspondence is a fundamental prerequisite for multimodal spectral imaging systems that fuse disparate wavelength ranges for subsequent...
By Eric L. Wisotzky, Jost Triller, Simon W. H\"artl, Oliver T. Bruns, Peter Eisert, Anna Hilsmann