  Synthetic data is a powerful tool in training data hungry deep learning
algorithms. However, to date, camera-based physiological sensing has not taken
full advantage of these techniques. In this work, we leverage a high-fidelity
synthetics pipeline for generating videos of faces with faithful blood flow and
breathing patterns. We present systematic experiments showing how
physiologically-grounded synthetic data can be used in training camera-based
multi-parameter cardiopulmonary sensing. We provide empirical evidence that
heart and breathing rate measurement accuracy increases with the number of
synthetic avatars in the training set. Furthermore, training with avatars with
darker skin types leads to better overall performance than training with
avatars with lighter skin types. Finally, we discuss the opportunities that
synthetics present in the domain of camera-based physiological sensing and
limitations that need to be overcome.
