Overfitting is a known issues in machine learning, people. If you still think all neural networks are doing is memorizing the dataset completely in the year 2021 - you might want to revisit the topic. It is one of the first concerns anyone training a deep model will have and to assume this model is overfit _without_ providing specific examples is arguing in bad faith.
Sentdex has shown his GAN is able to generalize various game logic like collision/friction with vehicles and also learns aspects of rendering such as a proper reflection of the sun on the back of the car.
He also showed weak points where the model is incapable of handling some situations and even did the impossible task of "splitting a car in two" to try and solve a head-on collision. Even though this is a failure case; it should at least provide you with some intuition that the GAN isn't just spitting out frames memorized from the dataset because that never happens in the dataset.
You will need to apply a little more rigor before outright dismissing these weights as merely overfit.
@sentdex Have you considered a guided diffusion approach now that that's all the rage? It's all rather new still but I believe it could be applied to these concepts as well.