  Video-based physiological signal estimation has been limited primarily to
predicting episodic scores in windowed intervals. While these intermittent
values are useful, they provide an incomplete picture of patients'
physiological status and may lead to late detection of critical conditions. We
propose a video Transformer for estimating instantaneous heart rate and
respiration rate from face videos. Physiological signals are typically
confounded by alignment errors in space and time. To overcome this, we
formulated the loss in the frequency domain. We evaluated the method on the
large scale Vision-for-Vitals (V4V) benchmark. It outperformed both shallow and
deep learning based methods for instantaneous respiration rate estimation. In
the case of heart-rate estimation, it achieved an instantaneous-MAE of 13.0
beats-per-minute.
