  We present a method for synthesizing naturally looking images of multiple
people interacting in a specific scenario. These images benefit from the
advantages of synthetic data: being fully controllable and fully annotated with
any type of standard or custom-defined ground truth. To reduce the
synthetic-to-real domain gap, we introduce a pipeline consisting of the
following steps: 1) we render scenes in a context modeled after the real world,
2) we train a human parsing model on the synthetic images, 3) we use the model
to estimate segmentation maps for real images, 4) we train a conditional
generative adversarial network (cGAN) to learn the inverse mapping -- from a
segmentation map to a real image, and 5) given new synthetic segmentation maps,
we use the cGAN to generate realistic images. An illustration of our pipeline
is presented in Figure 2. We use the generated data to train a multi-task model
on the challenging tasks of UV mapping and dense depth estimation. We
demonstrate the value of the data generation and the trained model, both
quantitatively and qualitatively on the CMU Panoptic Dataset.
