Diffusion models are real-time game engines

Could is certainly a better word, yes. There is no guarantee that it will happen, only that it could. The existence of LLMs is proof of that; imagine how large and inefficient a handwritten computer program to generate the next token would be. On the flipside, human beings very effectively predicting the next token, and much more, on 5 watts is proof that LLM in their current form certainly are not the most efficient method for generating next token.

I don't really know why everyone is piling on me here. Sorry for a bit of fun speculating! This model is on the continuum. There is a latent representation of Doom in weights. some weights, not these weights. Therefore some representation of doom in a neural net could become more efficient over time. That's really the point I'm trying to make.

I wonder if they could somehow feed in a trained Gaussian splats model to this to get better images?

Since the splats are specifically designed for rendering it seems like it would be an efficient way for the image model to learn the geometry without having to encode it on the image model itself.

Several thoughts for future work:

1. Continue training on all of the games that used the Doom engine to see if it is capable of creating new graphics, enemies, weapons, etc. I think you would need to embed more details for this perhaps information about what is present in the current level so that you could prompt it to produce a new level from some combination.

2. Could embedding information from the map view or a raytrace of the surroundings of the player position help with consistency? I suppose the model would need to predict this information as the neural simulation progressed.

3. Can this technique be applied to generating videos with consistent subjects and environments by training on a camera view of a 3D scene and embedding the camera position and the position and animation states of objects and avatars within the scene?

4. What would the result of training on a variety of game engines and games with different mechanics and inputs be? The space of possible actions is limited by the available keys on a keyboard or buttons on a controller but the labelling of the characteristics of each game may prove a challenge if you wanted to be able to prompt for specific details.

What is the point of this? It's hard to see how this is useful. Maybe it's just an exercise to show what a diffusion model can do?

Ehhh okay, I'm not as convinced as I was earlier. Sorry for misleading. There's been a lot of back-and-forth.

I would've really liked to see a section of the paper explicitly call out that they used humans in real time. There's a lot of sentences that led me to believe otherwise. It's clear that they used a bunch of agents to simulate gameplay where those agents submitted user inputs to affect the gameplay and they captured those inputs in their model. This made it a bit murky as to whether humans ever actually got involved.

This statement, "Our end goal is to have human players interact with our simulation. To that end, the policy π as in Section 2 is that of human gameplay. Since we cannot sample from that directly at scale, we start by approximating it via teaching an automatic agent to play"

led me to believe that while they had an ultimate goal of user input (why wouldn't they) they sufficed by approximating human input.

I was looking to refute that assumption later in the paper by hopefully reading some words on the human gameplay experience, but instead, under Results, I found:

"Human Evaluation. As another measurement of simulation quality, we provided 10 human raters with 130 random short clips (of lengths 1.6 seconds and 3.2 seconds) of our simulation side by side with the real game. The raters were tasked with recognizing the real game (see Figure 14 in Appendix A.6). The raters only choose the actual game over the simulation in 58% or 60% of the time (for the 1.6 seconds and 3.2 seconds clips, respectively)."

and it's like.. okay.. if you have a section in results on human evaluation, and your goal is to have humans play, then why are you talking just about humans reviewing video rather than giving some sort of feedback on the human gameplay experience - even if it's not especially positive?

Still, in the Discussion section, it mentions, "The second important limitation are the remaining differences between the agent’s behavior and those of human players. For example, our agent, even at the end of training, still does not explore all of the game’s locations and interactions, leading to erroneous behavior in those cases." which makes it more clear that humans gave input which went outside the bounds of the automatic agents. It doesn't seem like this would occur if it were agents simulating more input.

Ultimately, I think that the paper itself could've been more clear in this regard, but clearly the publishing website tries to be very explicit by saying upfront - "Real-time recordings of people playing the game DOOM" and it's pretty hard to argue against that.

Anyway. I repent! It was a learning experience going back and forth on my belief here. Very cool tech overall.