The interest in driver monitoring has grown recently, especially in the context of autonomous vehicles. However, the training of deep neural networks for computer vision requires more and more images with significant diversity, which does not match the reality of the field. This lack of data prevents networks to be properly trained for certain complex tasks such as human pose transfer which aims to produce an image of a person in a target pose from another image of the same person. To tackle this problem, we propose a new synthetic dataset for pose-related tasks. By using a straightforward pipeline to increase the variety between the images, we generate 200k images with a hundred human models in different cars, environments, lighting conditions, etc. We measure the quality of the images of our dataset and compare it with other datasets from the literature. We also train a network for human pose transfer in the synthetic domain using our dataset. Results show that our dataset matches the quality of existing datasets and that it can be used to properly train a network on a complex task. We make both the images with the pose annotations and the generation scripts publicly available.