Deep learning methods trained on 3D medical images typically do not generalize well as training data are relatively homogenous and small. One way to potentially overcome this issue is creating realistic-looking 3D medical images using generative models. This chapter describes the fundamental principles and architectures of generative models used for this purpose, such as those based on generative adversarial networks (GANs) and diffusion probabilistic models (DPMs). The chapter also reviews evaluation techniques for measuring the quality of synthetic medical images, including the evaluation of the biological plausibility of the anatomy displayed.