All good points. In this case, the original dataset is created from real world body scans. You collect enough scans in this "base collection of scans" to have a "real" distribution of the world. You can then span a latent space on top of this initial distribution and use GANs to further scale it. This isn't as good as real yet, but it generates results that are better than limited quantities of real data alone. Agree with your point around the Monte Carlo simulation. Synthetic data is not the be-all end-all to train neural networks.