We propose to augment MRXCAT with left-ventricular anatomies and function, representing realistic population statistics, to enable design and validation of CMR imaging and inference approaches. A variational autoencoder is deployed to identify parametric anatomical representations from a cohort of healthy and diseased anatomies to allow for the generation of new synthetic anatomies. Cardiac function is simulated using a biophysical model also incorporating local tissue defects. The resulting ventricular tissue masks are enriched with background information from XCAT. Synthetic MR images are generated using MRXCAT, making available paired data of CMR images and (patho)physiological parameters including ground-truth ventricular strains and displacements.