Camera-based physiological measurement is a growing field with neural models providing state-the-art-performance. Prior research have explored various "endto-end" models; however these methods still require several preprocessing steps. These additional operations are often non-trivial to implement making replication and deployment difficult and can even have a higher computational budget than the "core" network itself. In this paper, we propose two novel and efficient neural models for camera-based physiological measurement called EfficientPhys that remove the need for face detection, segmentation, normalization, color space transformation or any other preprocessing steps. Using an input of raw video frames, our models achieve state-of-the-art accuracy on three public datasets. We show that this is the case whether using a transformer or convolutional backbone. We further evaluate the latency of the proposed networks and show that our most light weight network also achieves a 33% improvement in efficiency. Camera-based physiological measurement is a non-contact approach for capturing cardiac signals via light reflected from the body. The most common such signal is the blood volume pulse (BVP) measured via the photoplethysmogram (PPG). From this, heart rate (Takano & Ohta, 2007; Verkruysse et al., 2008), respiration rate (Poh et al., 2010) and pulse transit times Shao et al. (2014) can be derived.