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Model

DeepPhys

convolutional attention network for video PPG

Code Cite
UnconfirmedCatalog paper date: 2018Source
CitationsView DOI record (opens in a new tab)GitHub starsView on GitHub (opens in a new tab)

Activity counts measure attention, not model quality. Missing counts are unknown. Publication status follows the linked source; a listed venue alone does not confirm peer review.

DeepPhys is a two-branch appearance-and-motion convolutional network with gated attention for video-based PPG (ECCV 2018; arXiv:1805.07888). Open implementations ship in the UW rPPG-Toolbox.

Form factor
camera_rppg
Openness
Open
Code
github.com/ubicomplab/rPPG-Toolbox
Year
2018
Published
ECCV 2018

arXiv: 1805.07888

At a glance
Type
Task model
Modalities
Not reported
Form factor
Camera rPPG
Parameters
Not reported
Architecture
Not reported
Venue
Not reported
Year
2018
arXiv
1805.07888 (opens in a new tab)
DOI
10.1007/978-3-030-01216-8_22 (opens in a new tab)
Code
Not reported
Weights
Not reported
Licence
Not reported
Caveats
Not reported
Datasets
Not reported
Added
Not reported
Updated
Oct 24, 2026

Abstract

Non-contact video-based physiological measurement has many applications in health care and human-computer interaction. Practical applications require measurements to be accurate even in the presence of large head rotations. We propose the first end-to-end system for video-based measurement of heart and breathing rate using a deep convolutional network. The system features a new motion representation based on a skin reflection model and a new attention mechanism using appearance information to guide motion estimation, both of which enable robust measurement under heterogeneous lighting and major motions. Our approach significantly outperforms all current state-of-the-art methods on both RGB and infrared video datasets. Furthermore, it allows spatial-temporal distributions of physiological signals to be visualized via the attention mechanism.

Verbatim fromarXiv· arXiv metadata (abstracts are released under CC0)

DeepPhys: ChatPPG research summary card

How it works

Inputs
DeepPhys
Outputs / tasks
Downstream tasks
Approach
rppg · model · deepphys

Datasets

No public datasets linked yet.

Cite

APA

DeepPhys. (2018). arXiv. https://doi.org/10.1007/978-3-030-01216-8_22

BibTeX

@misc{deepphys2018deepphys,
  title         = {{DeepPhys}},
  year          = {2018},
  doi           = {10.1007/978-3-030-01216-8_22},
  eprint        = {1805.07888},
  archivePrefix = {arXiv}
}

Built from the hub record. Check it against the publisher before submitting.

History

  1. Record updated

Software: rPPG-Toolbox is listed in Wearable Signal Tools, with its licence, commercial use and last commit.

Same tasks, data and authors