What professional colorists catch that ML engineers miss.
Three specific failure modes I see repeatedly in generative video, and why almost none of them are caught by evaluators without a post-production background. A short argument for domain expertise.
There is a particular way a generated frame can be wrong that I have started calling confident-but-flat. The lighting is plausible. The composition is recognizable. A model trained on ten billion parameters has produced something that, at a glance, looks like a photograph. And yet, to any working colorist, it reads immediately as not-quite-right. The failure is not in the pixels. It is in the argument the frame is making about its own light.
I spend a lot of my working week trying to articulate this failure to people whose training did not require them to articulate it. It is harder than it sounds.