Learning people detection models from few training samples

People detection is an important task for a wide range of applications in computer vision. State-of-the-art methods learn appearance based models requiring tedious collection and annotation of large data corpora. Also, obtaining data sets representing all relevant variations with sufficient accuracy for the intended application domain at hand is often a non-trivial task. Therefore this paper investigates how 3D shape models from computer graphics can be leveraged to ease training data generation. In particular we employ a rendering-based reshaping method in order to generate thousands of synthetic training samples from only a few persons and views. We evaluate our data generation method for two different people detection models. Our experiments on a challenging multi-view dataset indicate that the data from as few as eleven persons suffices to achieve good performance. When we additionally combine our synthetic training samples with real data we even outperform existing state-of-the-art methods.