The recent curation of large-scale databases with 3D surface scans of shapes has motivated the development of computational tools that better detect global patterns in morphological variation. Recent studies have focused on developing methods for the task of sub-image selection which aims at identifying physical features that best describe the variation between classes of 3D objects. A large piece in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes within these data are often small due to expensive collection procedures. Meanwhile, the landscape of current shape simulation methods has been mostly limited to approaches that use black-box inference---making it difficult to systematically assess the power and calibration of sub-image models. In this talk, we present a new statistical framework for simulating realistic 2D and 3D shapes based on probability distributions which can be learned from real data. We demonstrate this framework in two applications within computational biology: (1) cellular imagining of neutrophils and (2) mandibular molars from four different suborders of primates.