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.

本研究探索了使用全合成或合成增强真实数据的两种方法来解决多人二维姿势估计的问题，并研究了哪种方法更好地推广到真实数据，以及虚拟人物在训练损失中的影响。通过使用增强数据集，且不考虑训练损失中的合成数据，可以得到最佳结果。该研究还使用对抗性师生框架，发现不是所有的合成样本对训练都有同样的信息量，且信息量会随着训练阶段的不同而发生变化。