Yes. But the main issue is in the way they formulate the problem. Their output is always a transparency mask, which of course will never handle distortions.
Right. Things like this are why it’s difficult integrating AI into professional movie pipelines— they’re super complex in ways AI cannot (yet) replicate for very good reasons that seem superfluous or trivially replaceable by people not familiar with them.
People in ML have this kind of belief rooted in the bitter lesson, that everything will eventually sort itself out given enough scale and data. That often makes them ignore the nuances of particular problem domains. CC is the opposite of that, it's just impossible to do everything at once.
It’s certainly a big part of the ML scene, but to a slightly lesser extent, a cultural facet of development in general. It’s not all bad! Many people have solved problems that nobody in their right mind would have attempted knowing the nitty gritty details; often the problem they solved wasn’t the one they intended to solve, or they only solve one small subset of it, but were still valuable advancements. Unfortunately, that also leads to reinforcing some people’s Dunning-Krueger-fueled insistence that they can solve another field’s difficult problems with a few thought experiments, and the only reason it hasn’t already been solved is because nobody thought to ask a developer as smart as them to momentarily consider the problem. Non-developers in tech often bear the brunt of it: moving into design after a decade of dev work, that irritating mindset was one of the reasons I left tech altogether a couple years later.
youd have to train it to also generate and st map of the distortions but creating the ground truth version of that from the synthetic data would add a lot more to render. also its very easy to plausibly fake, its not something humans are good at seeing and knowing its wrong. you can tell its completely missing but accurate vs just distorted in a plausible way is not something most brains are tuned to notice.