Training a GAN to generate people without a significantly large dataset (if that's even possible) is probably just as difficult of a problem as just building th

Training a GAN to generate people without a significantly large dataset (if that's even possible) is probably just as difficult of a problem as just building the model you want in the end without sufficient data. Assuming those image sets are small they will create a model with a large bias. If you're talking about fine-tuning an existing model with small datasets, this is done already and works fairly well if not overused. It all comes down to: to create the first "data-generating" model you need a lot of data and compute. Expanding it is a different story, but that isn't where the problem lies. We come full-circle back to the same problem as what we started with: big players can afford to build these models and small players can't.