Apple recently released the simGAN paper focused on making simulated images look real-how? They used a particular GAN architecture, called simGAN, to improve images of eyeballs. Why is this problem interesting? Imagine realistic hands with no models needed. It provides a whole new avenue and revenue stream for many companies once these techniques can be replicated in real life. Using the simGAN architecture, you'll notice that the actual network architectures aren't that complicated:

The real secret sauce is in the loss function that the Apple developers used to train the networks. A loss function is how the GAN is able to know when to stop training the GAN. Here’s the powerful piece to this architecture: labeled real data can be expensive to produce or generate. In terms of time and cost, simulated data with perfect labels is easy to produce and the trade space is controllable.