Comprehensive analysis and implementation guides for state-of-the-art rPPG synthetic and augmentation methods addressing Fitzpatrick skin type disparities and motion artifacts
| Method | Overall MAE (bpm) |
Fitzpatrick I-II (bpm) |
Fitzpatrick V-VI (bpm) |
Fairness Score | Motion Robustness | Training Time (hours) |
GPU Memory (GB) |
|---|---|---|---|---|---|---|---|
|
PhysFlow
|
2.1±0.3 | 1.8±0.2 | 2.4±0.4 | 0.92 | 0.89 | 24-36 | 8-12 |
|
Neural Motion Transfer
|
3.2±0.6 | 2.9±0.5 | 3.6±0.8 | 0.85 | 0.92 | 36-48 | 10-14 |
|
DG-rPPGNet
|
2.5±0.4 | 2.2±0.3 | 2.9±0.5 | 0.90 | 0.87 | 18-24 | 6-8 |
|
InfoGAN
|
2.8±0.5 | 2.5±0.4 | 3.2±0.7 | 0.88 | 0.85 | 48-72 | 12-16 |
Conditional Normalizing Flows
Bidirectional skin tone transfer using conditional normalizing flows while preserving physiological signal characteristics.
Two-Stage Neural Rendering
Realistic motion synthesis while preserving physiological signal characteristics through neural rendering.
Domain Generalization
Disentangled feature learning with domain permutation for robust cross-demographic performance.
Information-Maximizing GAN
Controllable synthetic data generation with disentangled representations of physiological and demographic factors.
Complete technical implementation guides with code, datasets, and step-by-step instructions for reproducing all methods.
Get started quickly with pre-configured environments and sample datasets.
git clone https://github.com/rppg-methods/implementations
cd implementations
conda env create -f environment.yml
python train_physflow.py --config configs/physflow.yaml
Comprehensive guide for preparing and preprocessing rPPG datasets with Fitzpatrick labeling.
Advanced training techniques, hyperparameter optimization, and performance tuning.
Comprehensive evaluation frameworks and benchmarking tools for fair comparison.