[8n4vidtmkvmk]

Right, but I'm not actually suggesting we use diffusion. At least, not the same models we're using now. We need to incorporate a few sample rays at least so that it 'knows' what's actually off-screen, and then we just give it lots of training data of partially rendered images and fully rendered images so that it learns how to fill in the gaps. It shouldn't hallucinate very much if we do that. I don't know how to solve for temporal coherence though -- I guess we might want to train on videos instead of still images.

Also, that new Google paper where it generates entire games from a single image has up to 60 seconds of 'memory' I think they said, so I don't think the "forgetting" is actually that big of a problem since we can refresh the memory with a properly rendered image at least every that often.

I'm just spitballing here though, I think all of Unreal 5.4 or 5.5 has put this into practice already with their new lighting system.

[jsheard]

> We need to incorporate a few sample rays at least so that it 'knows' what's actually off-screen, and then we just give it lots of training data of partially rendered images and fully rendered images so that it learns how to fill in the gaps.

That's already a thing, there's ML-driven denoisers which take a rough raytraced image and do their best to infer what the fully converged image would look like based on their training data. For example in the offline rendering world there's Nvidia's OptiX denoiser and Intel's OIDN, and in the realtime world there's Nvidia's DLSS Ray Reconstruction which uses an ML model to do both upscaling and denoising at the same time.

https://developer.nvidia.com/optix-denoiser

https://www.openimagedenoise.org