In this paper we propose and validate a PCA-based respiratory motion model for motion compensation during image-guided cardiac interventions. In a preparatory training phase, a preoperative 3-D segmentation of the coronary arteries is automatically registered with a cardiac gated biplane cineangiogram, and used to build a respiratory motion model. This motion model is subsequently used as a prior within the intraoperative registration process for motion compensation to restrict the search space. Our hypothesis is that the use of this model-constrained registration increases the robustness and registration accuracy, especially for weak data constraints such as low signal-to-noise ratio, the lack of contrast information, or an intraoperative monoplane setting. This allows for reducing radiation exposure without compromising on registration accuracy. Synthetic data as well as phantom and clinical datasets have been used to validate the model-based registration in terms of registration accuracy, robustness and speed. We were able to significantly accelerate the intraoperative registration with a 3-D TRE of less than 2 mm for both monoplane images and intraprocedure settings with missing contrast information based on 2-D guidewire tracking, which makes it feasible for motion correction in clinical procedures.