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...
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.
arXiv:2606. 12378v1 Announce Type: cross Abstract: Physiological awareness is important for service, social, and assistive robots that interact with humans in everyday environments.
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.
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: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.
Facial movements convey subtle and important information that is critical for human social communication. Optical methods for face capture are difficult or impossible to use when the face is occluded...
The paper presents emg2face, a system that uses high‑density surface electromyography (HD‑sEMG) to capture facial expressions without optical cameras, addressing issues of occlusion and privacy. It records 64 EMG channels with textile grids, synchronizes the data with video using analog audio bursts, and fits a high‑resolution parametric head model to 3D facial landmarks. A deep neural network then predicts blendshape parameters from the EMG signals, enabling real‑time facial animation on various characters.
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...
arXiv:2610.10945v1 Announce Type: cross Abstract: We present GHARP (Real-time Gaussian Head Animation from Large-scale Reconstruction Prior), a method that animates 3D human heads in real time from a...
arXiv:2609.12668v1 Announce Type: new Abstract: Recent deepfake detection studies increasingly suggest remote photoplethysmography (rPPG) signals as an authenticity cue. However, existing benchmarks...
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...