The treatment of burns is supported by accurately estimating measurements such as total body surface area burned (TBSA-b) and total body surface area (TBSA). Computing these values automatically can lead to faster decisions and reduce human error. Deep learning-based methods rely on big data to perform well in practical applications. This is especially true for burn medicine, where real data is scarce. In this paper, we present a pipeline for synthesizing diagnostic burn images and wound annotations from virtual 3D models. We demonstrate how to generate a heterogeneous dataset by combining such features as body shape, real skin and wound textures, background scenes, camera settings and illumination. The resulting images can be used for various deep-learning tasks such as wound detection, segmentation or classification. Solely with these synthetic images, we train models for burn wound segmentation and show that they learn distinctive features of burn wounds.