  Machine learning models for camera-based physiological measurement can have
weak generalization due to a lack of representative training data. Body motion
is one of the most significant sources of noise when attempting to recover the
subtle cardiac pulse from a video. We explore motion transfer as a form of data
augmentation to introduce motion variation while preserving physiological
changes of interest. We adapt a neural video synthesis approach to augment
videos for the task of remote photoplethysmography (rPPG) and study the effects
of motion augmentation with respect to 1) the magnitude and 2) the type of
motion. After training on motion-augmented versions of publicly available
datasets, we demonstrate a 47% improvement over existing inter-dataset results
using various state-of-the-art methods on the PURE dataset. We also present
inter-dataset results on five benchmark datasets to show improvements of up to
79% using TS-CAN, a neural rPPG estimation method. Our findings illustrate the
usefulness of motion transfer as a data augmentation technique for improving
the generalization of models for camera-based physiological sensing. We release
our code for using motion transfer as a data augmentation technique on three
publicly available datasets, UBFC-rPPG, PURE, and SCAMPS, and models
pre-trained on motion-augmented data here: https://motion-matters.github.io/
