  Remote photoplethysmography (rPPG) based physiological measurement is an
emerging yet crucial vision task, whose challenge lies in exploring accurate
rPPG prediction from facial videos accompanied by noises of illumination
variations, facial occlusions, head movements, \etc, in a non-contact manner.
Existing mainstream CNN-based models make efforts to detect physiological
signals by capturing subtle color changes in facial regions of interest (ROI)
caused by heartbeats. However, such models are constrained by the limited local
spatial or temporal receptive fields in the neural units. Unlike them, a native
Transformer-based framework called Dual-path TokenLearner (Dual-TL) is proposed
in this paper, which utilizes the concept of learnable tokens to integrate both
spatial and temporal informative contexts from the global perspective of the
video. Specifically, the proposed Dual-TL uses a Spatial TokenLearner (S-TL) to
explore associations in different facial ROIs, which promises the rPPG
prediction far away from noisy ROI disturbances. Complementarily, a Temporal
TokenLearner (T-TL) is designed to infer the quasi-periodic pattern of
heartbeats, which eliminates temporal disturbances such as head movements. The
two TokenLearners, S-TL and T-TL, are executed in a dual-path mode. This
enables the model to reduce noise disturbances for final rPPG signal
prediction. Extensive experiments on four physiological measurement benchmark
datasets are conducted. The Dual-TL achieves state-of-the-art performances in
both intra- and cross-dataset testings, demonstrating its immense potential as
a basic backbone for rPPG measurement. The source code is available at
\href{https://github.com/VUT-HFUT/Dual-TL}{https://github.com/VUT-HFUT/Dual-TL}
