PhysVR: Vision-Language Model Guided Interference-aware Temporal Feature Refinement for Remote Physiological Measurement

• URL: http://arxiv.org/abs/2608.29663v1
• Date: Sun, 30 Aug 2026 08:56:46 GMT
• Title: PhysVR: Vision-Language Model Guided Interference-aware Temporal Feature Refinement for Remote Physiological Measurement
• Abstract summary: We present PhysVR, a vision-language model guided interference-aware temporal feature refinement framework for r estimation.<n>In parallel, a frozen vision-language model processes sampled facial frames under an interference-oriented prompt, and an evidence head extracts visual interference evidence from the VLM output.<n>Experiments on five public benchmarks demonstrate that PhysVR consistently outperforms representative methods under both intra-dataset and cross-dataset evaluation protocols.
• Score: 30.385089857503207
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Remote photoplethysmography (rPPG) enables contactless physiological measurement from facial videos, yet its subtle pulse-related variations are easily affected by illumination variation, head motion, facial blur, and region-of-interest instability. Existing methods mainly suppress interference during feature learning, while whether the learned temporal features remain affected by interference and how to further suppress such interference before rPPG estimation are rarely examined. To address this limitation, we propose PhysVR, a vision-language model guided interference-aware temporal feature refinement framework for rPPG estimation. Specifically, a physiological backbone produces global temporal features and a coarse rPPG prediction, from which signal-derived physiological reliability evidence is constructed from local temporal characteristics. In parallel, a frozen vision-language model processes sampled facial frames under an interference-oriented prompt, and an evidence head extracts visual interference evidence from the VLM output. Temporal cross-attention integrates the physiological and visual evidence with the global temporal features to construct interference-aware temporal context. Guided by this context, a shared temporal correction unit performs general refinement, while four interference-specific experts selectively suppress different interference through adaptive routing. The refined temporal features are then used for final rPPG estimation. Extensive experiments on five public benchmarks demonstrate that PhysVR consistently outperforms representative methods under both intra-dataset and cross-dataset evaluation protocols.

Related papers

• Closed-loop coupling of personalised and foundation models for real-time treatment guidance with MRI [0.0]
  Image-guided therapies rely on real-time targeting of anatomical structures.<n>In the presence of motion, imaging latencies create a temporal misalignment between observed and true anatomy, compromising treatment accuracy.<n>Here we introduce a closed-loop coupling framework that synergises patient-specific temporal prediction with continuous segmentation-based anatomical interpretation.
  arXiv  Detail & Related papers  (2026-07-01T06:32:10Z)
• Distilling Temporal Coherence into 2D Networks for Transrectal Ultrasound Prostate Video Segmentation [2.65131397683555]
  Real-time video segmentation of the prostate in Transrectal Ultrasound (TRUS) is essential for image-guided interventions.<n>We present a Temporally Consistent Learning Framework that distills temporal coherence into a 2D network during training.<n>Our design is driven by a key clinical observation: the prostate exhibits geometric stability, whereas the surrounding acoustic environment fluctuates due to physiological motion and transducer pressure.
  arXiv  Detail & Related papers  (2026-06-30T06:27:02Z)
• BCI-Based Assessment of Ocular Response Time Using Dynamic Time Warping Leveraging an RDWT-Driven Deep Neural Framework [1.7883249184785495]
  We present an initial framework that integrates electroencephalogram (EEG) with augmented-reality (AR)-based Vestibular/Ocular Motor Screening (VOMS) tasks.<n>EEG signals are analyzed using a Redundant Discrete Wavelet Transform (RDWT)-driven deep neural framework.<n> Sliding-window predictions were validated using Pearson correlation, and Dynamic Time Warping (DTW) was subsequently used to estimate ocular response times.
  arXiv  Detail & Related papers  (2026-05-14T14:28:11Z)
• Intervention-Based Self-Supervised Learning: A Causal Probe Paradigm for Remote Photoplethysmography [38.62089038790769]
  We propose a new SSL paradigm, Causal Probing (PCP)<n>PCP treats the latent r signal as the underlying physical source and the resulting pixel chrominance variations as its visual manifestation.<n>Our editor achieves both in-domain and cross-domain performance on challenging datasets such as VIPL-HR and MMPD-HR.
  arXiv  Detail & Related papers  (2026-04-26T09:56:09Z)
• FreqPhys: Repurposing Implicit Physiological Frequency Prior for Robust Remote Photoplethysmography [54.32412212867472]
  Photoplethysmography enables contactless physiological monitoring by capturing subtle skin-color variations from videos.<n>FreqPhys is a frequency-guided rulation framework that explicitly leverages physiological frequency priors for robust signal recovery.<n>It yields significant improvements over state-of-the-art approaches, particularly under challenging motion conditions.
  arXiv  Detail & Related papers  (2026-04-01T06:25:42Z)
• Unleashing Temporal Capacity of Spiking Neural Networks through Spatiotemporal Separation [67.69345363409835]
  Spiking Neural Networks (SNNs) are considered naturally suited for temporal processing, with membrane potential propagation widely regarded as the core temporal modeling mechanism.<n>We design Non-Stateful (NS) models progressively removing membrane propagation to its stage-wise role. Experiments reveal a counterintuitive phenomenon: moderate removal in shallow layers improves performance, while excessive removal causes collapse.
  arXiv  Detail & Related papers  (2025-12-05T07:05:53Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [42.60778405812048]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• Remote Photoplethysmography in Real-World and Extreme Lighting Scenarios [26.913899198659436]
  We propose an end-to-endsupervised model for remote photoplemography.<n>It strives to eliminate complex and unknown external time-varying interferences.<n>This is the first robust r model for real outdoor natural face videos.
  arXiv  Detail & Related papers  (2025-03-14T14:50:58Z)
• On the Identification of Temporally Causal Representation with Instantaneous Dependence [50.14432597910128]
  Temporally causal representation learning aims to identify the latent causal process from time series observations. Most methods require the assumption that the latent causal processes do not have instantaneous relations. We propose an textbfIDentification framework for instantanetextbfOus textbfLatent dynamics.
  arXiv  Detail & Related papers  (2024-05-24T08:08:05Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• PhysFormer++: Facial Video-based Physiological Measurement with SlowFast Temporal Difference Transformer [76.40106756572644]
  Recent deep learning approaches focus on mining subtle clues using convolutional neural networks with limited-temporal receptive fields. In this paper, we propose two end-to-end video transformer based on PhysFormer and Phys++++, to adaptively aggregate both local and global features for r representation enhancement. Comprehensive experiments are performed on four benchmark datasets to show our superior performance on both intra-temporal and cross-dataset testing.
  arXiv  Detail & Related papers  (2023-02-07T15:56:03Z)
• PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer [55.936527926778695]
  Recent deep learning approaches focus on mining subtle r clues using convolutional neural networks with limited-temporal receptive fields. In this paper, we propose the PhysFormer, an end-to-end video transformer based architecture.
  arXiv  Detail & Related papers  (2021-11-23T18:57:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.