The culture was and is skeptical in biased manners. Between '04 and '08 I worked with a group that had trained neural nets for 3D reconstruction of human heads. They were using it for prenatal diagnostics and a facial recognition pre-processor, and I was using it for creating digital doubles in VFX film making. By '08 I'd developed a system suitable for use in mobile advertising, creating ads with people in them, and 3D games with your likeness as the player. VCs thought we were frauds, and their tech advisors told them our tech was an old discredited technique that could not do what we claimed. We spoke to every VC, some of which literally kicked us out. Finally, after years of "no" that same AlexNet success begins to change minds, but now they want the tech to create porn. At that point, after years of "no" I was making children's educational media, there was no way I was gonna do porn. Plus, president of my co was a woman, famous for creating children's media. Yeah, the culture was different then, not too long ago.
Who's offering VC money for neural network porn technology? As far as I can tell, there is huge organic demand for this but prospective users are mostly cheapskates and the area is rife with reputational problems, app store barriers, payment processor barriers, and regulatory barriers. In practice I have only ever seen investors scared off by hints that a technology/platform would be well matched to adult entertainment.
Wow, so early for generative -- although I assume you were generating parameters that got mapped to mesh positions, rather than generating pixels?
I definitely remember that bias about neural nets, to the point of my first grad ML class having us recreate proofs that you should never need more than two hidden layers (one can pick up the thread at [1]). Of all the ideas clunking around in the AI toolbox at the time, I don't really have background on why people felt the need to kill NN with fire.
It was annotated face images and 3D scans of heads trained to map one to the other. After a threshold in the size of the training data, good to great results from a single photo could be had to generate the mesh 3D positions, and then again to map the photo onto the mesh surface. Do that with multiple frames, and one is firmly in the Uncanny Valley.
