This paper presents CliffPhys, a family of models that leverage hypercomplex neural architectures for camera-based respiratory measurement. The proposed approach extracts respiratory motion from standard RGB cameras, relying on optical flow and monocular depth estimation to obtain a 2D vector field and a scalar field, respectively. We show how the adoption of Clifford Neural Layers to model the geometric relationships within the recovered input fields allows to effectively recover respiratory information. Experimental results in three publicly available datasets demonstrate CliffPhys' superior performance compared to both baselines and recent neural approaches, achieving state-of-the-art results in the prediction of respiratory rates. Source code available at: https://anonymous.4open.science/r/CliffPhys-2D26/.