Even a simpler task like image classification such as: does the picture contain a lion.
Imagine you have 3d model of a lion. You can render it from lots of different angles, lighting conditions, backgrounds, stretched out, curled up, etc. You know the ground truth classification on all renderings is that the picture contains a lion, but being able to generate images of lions is a very different task from recognizing lions in images.
The potential issue with using synthetic data to simulate the problem (like image classification) is that recognizing a lion in synthetic imagery and recognizing a lion in real imagery may also be very different tasks to a computer.
not that much actually
because the image are handled at a lower resolution and with a smaller set of colors, to make the models train faster(a lot faster)
so they look pretty much the same to a computer vision algorithm