  The use of cameras and computational algorithms for noninvasive, low-cost and
scalable measurement of physiological (e.g., cardiac and pulmonary) vital signs
is very attractive. However, diverse data representing a range of environments,
body motions, illumination conditions and physiological states is laborious,
time consuming and expensive to obtain. Synthetic data have proven a valuable
tool in several areas of machine learning, yet are not widely available for
camera measurement of physiological states. Synthetic data offer "perfect"
labels (e.g., without noise and with precise synchronization), labels that may
not be possible to obtain otherwise (e.g., precise pixel level segmentation
maps) and provide a high degree of control over variation and diversity in the
dataset. We present SCAMPS, a dataset of synthetics containing 2,800 videos
(1.68M frames) with aligned cardiac and respiratory signals and facial action
intensities. The RGB frames are provided alongside segmentation maps. We
provide precise descriptive statistics about the underlying waveforms,
including inter-beat interval, heart rate variability, and pulse arrival time.
Finally, we present baseline results training on these synthetic data and
testing on real-world datasets to illustrate generalizability.
