This repository is the official implementation of Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner that has been accepted to ECCV 2020.
Left to right:
- Cropped input image
- End-to-end trained model (baseline)
- Meta-rPPG (transducive inference)
- Top to down: rPPG signal, Power Spectral Density (PSD), Predicted and ground truth heart rate
To install requirements:
pip install -r requirements.txt
All experiments can be run on a single NVIDIA GTX1080Ti GPU.
The code was tested with python3.6 the following software versions:
| Software | version |
|---|---|
| cuDNN | 7.6.5 |
| Pytorch | 1.5.0 |
| CUDA | 10.2 |
Download training data (example.pth) from Google Drive. Due to privacy issue (face images), provided data contains only a subset of the entire training data, i.e. contains faces of the authors of this paper.
Move example.pth to data/ directory:
mv example.pth data/
To begin training, run:
python3 train.py
Validation data can be requested from:
If you find this work useful, consider citing our work using the following bibTex:
@inproceedings{lee2020meta,
title={Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner},
author={Lee, Eugene and Chen, Evan and Lee, Chen-Yi},
booktitle={European Conference on Computer Vision (ECCV)},
year={2020}
}