  Recent advances in supervised deep learning techniques have demonstrated the
possibility to remotely measure human physiological vital signs (e.g.,
photoplethysmograph, heart rate) just from facial videos. However, the
performance of these methods heavily relies on the availability and diversity
of real labeled data. Yet, collecting large-scale real-world data with
high-quality labels is typically challenging and resource intensive, which also
raises privacy concerns when storing personal bio-metric data. Synthetic
video-based datasets (e.g., SCAMPS \cite{mcduff2022scamps}) with
photo-realistic synthesized avatars are introduced to alleviate the issues
while providing high-quality synthetic data. However, there exists a
significant gap between synthetic and real-world data, which hinders the
generalization of neural models trained on these synthetic datasets. In this
paper, we proposed several measures to add real-world noise to synthetic
physiological signals and corresponding facial videos. We experimented with
individual and combined augmentation methods and evaluated our framework on
three public real-world datasets. Our results show that we were able to reduce
the average MAE from 6.9 to 2.0.
