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Contrast-Phys is an unsupervised contrastive learning method for remote PPG from face video (ECCV 2022), with Contrast-Phys+ follow-on work. Official MIT-licensed code is at github.com/zhaodongsun/contrast-phys.
- Form factor
- camera_rppg
- License
- MIT
- Openness
- Open
- Code
- github.com/zhaodongsun/contrast-phys
- Published
- ECCV 2022
- Type
- Task model
- Modalities
- Not reported
- Form factor
- Camera rPPG
- Parameters
- Not reported
- Architecture
- Not reported
- Venue
- Not reported
- Year
- Not reported
- arXiv
- Not reported
- DOI
- Not reported
- Code
- Not reported
- Weights
- Not reported
- Licence
- MIT (opens in a new tab)
- Caveats
- Not reported
- Datasets
- Not reported
- Added
- Not reported
- Updated
- Oct 24, 2026
Original description
Update: The paper for Contrast-Phys+ has been accepted by TPAMI. Please check the contrast-phys+ folder for the code.
We incorporate prior knowledge about remote photoplethysmography (rPPG) into contrastive learning to achieve unsupervised rPPG training. The method does not require any ground truth for training.
Please check requirement.txt for the required Python libraries.
Verbatim fromGitHub· Description from the repository README; licence: MIT
How it works
- Approach
- rppg · model · contrast phys · unsupervised · mit
Datasets
Cite
APA
Contrast-Phys / Contrast-Phys+. (n.d.).
BibTeX
@misc{contrastphyscontrastphys,
title = {{Contrast-Phys / Contrast-Phys+}}
}Built from the hub record. Check it against the publisher before submitting.
History
Record updated
- PALC-NetShared topic: unsupervisedphysiology-aware lightweight contrastive network for unsupervised rPPGRGB2026
- PHA + FDPShared topic: unsupervisedprogressive hierarchical attention and frequency-decoupled loss for unsupervised rPPGRGB2026
- ME-rPPGShared topic: mitmemory-efficient real-time rPPG via temporal-spatial state space duality2025
- RhythmFormerShared topic: mitperiodic sparse attention Transformer for rPPG2024