No, I am not. The interaction is part of the training, and is used during inference, but it is not including during the process of generation.

Okay, I think you're right. My mistake. I read through the paper more closely and I found the abstract to be a bit misleading compared to the contents. Sorry.

Don't worry. The paper is not very well written.

Academic authors are consistently better at editing away unclear and ambiguous statements which make their work seem less impressive compared to ones which make their work seem more impressive. Maybe it's just a coincidence, lol.

It's interactive but can it go beyond what it learned from the videos. As in, can the camera break free and roam around the map from different angles? I don't think it will be able to do that at all. There are still a few hallucinations in this rendering, it doesn't look it understands 3d.

You might be surprised. Generating views from novel angles based on a single image is not novel, and if anything, this model has more than a single frame as input. I’d wager that it’s quite able to extrapolate DOOM-like corridors and rooms even if it hasn’t seen the exact place during training. And sure, it’s imperfect but on the other hand it works in real time on a single TPU.

Then why do monsters become blurry smudgy messes when shot? That looks like a video compression artifact of a neural network attempting to replicate low-structure image (source material contains guts exploding, very un-structured visual).

Uh, maybe because monster death animations make up a small part of the training material (ie. gameplay) so the model has not learned to reproduce them very well?

There cannot be "video compression artifacts" because it hasn’t even seen any compressed video during training, as far as I can see.

Seriously, how is this even a discussion? The article is clear that the novel thing is that this is real-time frame generation conditioned on the previous frame(s) AND player actions. Just generating video would be nothing new.

In a sense, poorly reproducing rare content is a form of compression artifact. Ie, since this content occurs rarely in the training set, it will have less impact on the gradients and thus less impact on the final form of the model. Roughly speaking, the model is allocating fewer bits to this content, by storing less information about this content in its parameters, compared to content which it sees more often during training. I think this isn't too different from certain aspects of images, videos, music, etc., being distorted in different ways based on how a particular codec allocates its available bits.

I simply cannot take seriously anyone who exclaims that monster death animations are a minor part of Doom. It's literally a game about slaying demons. Gameplay consists almost entirely of explosions and gore, killing monsters IS THE GAME, if you can't even get that correct then what nonsense are we even looking at.

Maybe it's so advanced, it knows the players' next moves, so it is a video!

I guess you are being sarcastic, except this is precisely what it is doing. And it's not hard: player movement is low information and probably not the hardest part of the model.