Yeah initial impression was that developers provide the training screenshots but the latest Q&A indicates that the "DLSS team", which I'm assuming is Nvidia, extracts screenshots from the game. It's all written by PR folks so it's hard to be sure exactly what the process is between the devs and NV.
Aren't they there for gaming as well for faster de-noising? I don't think anyone is using them yet but we don't exactly have a lot of RTX enhanced titles.
In gaming, the tensor cores are only being used for DLSS AFAIK.
its actually pretty fast I think, at least relatively speaking to the total development time of the title. The relatively very little labour as well comparatively to the labour costs of implementing another AA technique. The only thing missing is the quality which nvidia will be working on.
I agree 100% that DLSS looks terribly blurry right now and, in metro's case, nothing at all like 4K - but there are some positives as well that should be mentioned. It looks more temporally stable than TAA in both games and does not ghost on things like GPU particles or other alpha.
This is where using the learning model can help: they can use the pixel inputs rather than just the final colors to decide what to do.
Better if someone prefers blurry images, but it seems i am the only person in the world who does.
Interestingly at least on top of surfaces where RT is used leaves and particles cause ghosting even on DLSS.Better if someone prefers blurry images, but it seems i am the only person in the world who does.
Looking at the turret at given time in the video, DLSS has more crawilng to me, so i would not call it more stable.
Curious if NV manages to fix this and the sharpness as well... maybe there will be still some players left then.
Otherwise they better find another application for the silicon.
Maybe that's ghosting coming from RT denoising? I would not think upscaling can cause or prevent ghosting at all?
RT denoising would be my guess.
You mean 10% faster, right?
I'll try to find the link but read that they "update" existing trained models with new samples. I guess the same principle used in AI training and inferencing, and will likely be explained in greater detail when the developers & Nvidia share their experiences.
Warning, cooking analogy up ahead! lol
So it might be like a stew. You serve it up, and it gets some criticism. Rather than throw the whole batch out, you spice it up, add in more broth so as to dilute its saltiness, and add in some pasta to give it texture.
