  Neural networks need big annotated datasets for training. However, manual
annotation can be too expensive or even unfeasible for certain tasks, like
multi-person 2D pose estimation with severe occlusions. A remedy for this is
synthetic data with perfect ground truth. Here we explore two variations of
synthetic data for this challenging problem; a dataset with purely synthetic
humans and a real dataset augmented with synthetic humans. We then study which
approach better generalizes to real data, as well as the influence of virtual
humans in the training loss. Using the augmented dataset, without considering
synthetic humans in the loss, leads to the best results. We observe that not
all synthetic samples are equally informative for training, while the
informative samples are different for each training stage. To exploit this
observation, we employ an adversarial student-teacher framework; the teacher
improves the student by providing the hardest samples for its current state as
a challenge. Experiments show that the student-teacher framework outperforms
normal training on the purely synthetic dataset.
