Earlier quoted context omitted.
The GAN model is the game environment. You're playing a neural network. The novelty is no game engine, no rules, just learned how to represent the game and you can play it.
What he meant is you overfitted the network with video footage. There is no game, just seemingly clever stitching and playback of learned footage similar concept applied to animations and implemented in a state machine https://www.youtube.com/watch?v=KSTn3ePDt50 and optimized with nn https://www.youtube.com/watch?v=16CHDQK4W5k
The second one is indeed interesting research and seems to be a combination of the prior learned motion mapping working in tandem with a generative model.
I suppose you could say that the automation of the dataset is considered as "augmentation"; but the difference here is that the dataset is just pixels and inputs rather than all that animation info and simulation data. Yes, a simulation is running; but the GAN only gets the pixels and the input.
There's a similarity there though; you're right. In either case; the explicit goal of the video you posted is to combat runtime constraints of generative models. I'm not certain it's a fair comparison.
The latter video and sentdex's result both seem to generalize to unique scenarios not present in the training set. This may mean they are creating an efficient representation of the underlying data in order to predict future samples more easily than simply overfitting.
The top level comment here is a shallow dismissal and Randomoneh could have answered these questions themselves before throwing out a smug comment like "I fail to see novelty here" when it's at the very least the first large-scale GAN successfully trained on GTA V.