Qwen image and nano banana can both do that with images, there’s zero reason to think we can’t train video models for masking.
This feels a lot like critiquing stable diffusion over hands and text, which the new SOTA models all handle well.
One of the easiest iterations on these models is to add more training cases to the benchmarks. That’s a timeline of months, not comparable to forecasting progress over 20 years like fusion.
Is it now. I don't think being able to accurately and predictably make changes to a shot, a draft, a design is surface level in production.
> Qwen image and nano banana can both do that with images, there’s zero reason to think we can’t train video models for masking.
Tell them to change the tilt of the camera roughly 15 degree left without changing anything else in the scene and tell me if it works.
> This feels a lot like critiquing stable diffusion over hands and text, which the new SOTA models all handle well.
Well does a lot of heavy lifting there.
> One of the easiest iterations on these models is to add more training cases to the benchmarks. That’s a timeline of months, not comparable to forecasting progress over 20 years like fusion.
And what if the model itself is the limiting factor ? The entire tech ? Do we have any proof that in the future the current technologies might be able to handle the cases I spoke about ?
Also, one thing that I didn't mention in the first post. Assuming that the tech does come to the point I can be used to automate a lot of the production. If Throwing a few millions to buy a GPU cluster is enough to be able to "generate" a relatively high quality movie or series, the barrier to entry will be incredibly low. The cost will be driven down, the amount of production will be very high and overall it might not be a trillion dollar industry no more.
Qwen image and nano banana can both do that with images, there’s zero reason to think we can’t train video models for masking.
This feels a lot like critiquing stable diffusion over hands and text, which the new SOTA models all handle well.
One of the easiest iterations on these models is to add more training cases to the benchmarks. That’s a timeline of months, not comparable to forecasting progress over 20 years like fusion.