Robustly detecting people in real world scenes is a fundamental and challenging task in computer vision. State-of-the-art approaches use powerful learning methods and manually annotated image data. Importantly, these learning based approaches rely on the fact that the collected training data is representative of all relevant variations necessary to detect people. Rather than to collect and annotate ever more training data, this paper explores the possibility to use a 3D human shape and pose model from computer graphics to add relevant shape information to learn more powerful people detection models. By sampling from the space of 3D shapes we are able to control data variability while covering the major shape variations of humans which are often difficult to capture when collecting real-world training images. We evaluate our data generation method for a people detection model based on pictorial structures. As we show on a challenging multi-viewpoint dataset, the additional information contained in the 3D shape model helps to outperform models trained on image data alone (see e.g. Fig. 1).