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
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
arXiv:2608. 15831v1 Announce Type: cross Abstract: Remote photoplethysmography (rPPG) enables non-contact heart rate (HR) monitoring from facial videos, but RGB-only methods are vulnerable to illumination changes, motion artifacts, and skin-tone-dependent optical reflectance.
By Bo Zhao, Zheng Wu, Yiping Xie, Zitong YU
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.
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:2609.12668v1 Announce Type: new
Abstract: Recent deepfake detection studies increasingly suggest remote photoplethysmography (rPPG) signals as an authenticity cue. However, existing benchmarks...
By Chenxi Yang, Yassine Ouzar, Larbi Boubchir
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.
arXiv:2607. 15995v1 Announce Type: cross Abstract: Deep remote photoplethysmography (rPPG) attains sub-bpm heart-rate error on frontal, stationary faces yet degrades sharply under head pose: on MMPD, the state-of-the-art FactorizePhys backbone's MAE grows $1.
By Hui Wei, Seyedata Jodeiri Seyedian, Xiaobai Li, Guoying Zhao
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.
arXiv:2606. 13839v1 Announce Type: cross Abstract: Remote photoplethysmography (rPPG) transformers achieve low heart-rate error on benchmarks, yet their decisions remain opaque--a growing concern as rPPG moves toward clinical heart rate estimation.
By Louis Chen, Torbj\"orn E. M. Nordling
arXiv:2609.24158v1 Announce Type: new
Abstract: Reconstructing expressive and relightable 3D head avatars from monocular videos remains challenging in computer vision, as it requires accurate modelin...
By Jiankuo Zhao, Xiangyu Zhu, Jijie Li, Baiqin Wang, Shukai Chen, Zhen Lei
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