Presumably de_dust2

Marble renders a static Gaussian Splat asset (like a 3D game engine asset) that you then render in a game engine.

Marble seems useful for lots of use cases - 3D design, online games, etc. You pay the GPU cost to render once, then you can reuse it.

Genie seems revolutionary but expensive af to render and deliver to end users. You never stop paying boatloads of H100 costs (probably several H100s or TPU equivalents per user session) per second.

You could make a VRChat type game with Marble.

You could make a VRChat game with Genie, but only the billionaires could afford to play it.

To be clear, Genie does some remarkably cool things. You can prompt it, "T-Rex tap dancing by" and it'll appear animated in the world. I don't think any other system can do this. But the cost is enormous and it's why we don't have a playable demo.

When the cost of GPU compute comes down, I'm sure we'll all be steaming a Google Stadia like experience of "games" rendered on the fly. Multiplayer, with Hollywood grade visuals. Like playing real time Lord of the Rings or something wild.

Interestingly, there is a model like Google Genie that is open source and available to run on your local Nvidia desktop GPU. It's called DiamondWM [1], and it's a world model trained on FPS gameplay footage. It generates a 10 fps 160x160 image you can play through. Maybe we'll develop better models and faster techniques and the dream of local world models can one day be realized.

[1] https://diamond-wm.github.io/

All of these have fairly "exact" representations, and generation techniques are also often fairly "exact" in trying to create worlds that won't break physics engines(big part) or rendering engines, often hand-crafted algorithms but nothing really that really stopped neural networks from being used on a higher level.

One important detail in most generation systems in games is that they are often built to be controllable to work with game-logic (think how Minecraft generates the world to include biomes,villages,etc) or more or less artist controllable.

3d scanning has often relied on point-clouds, but were heavy, full of holes,etc and have been infeasible for direct rendering for long so many methods were developed to make decent polygon meshes.

Nerf's and Gaussian splatting(GS) started appearing a few years back, these are more "approximate" and totally ignore polygon generation instead relying on quantization of the world into NN-matrix-"fields"(NERF) or fuzzy-point-clouds (GS), visually these have been impressive since they managed to capture "real" images well.

This system is built on GS since that probably meshed fairly well with neural network token and diffusion techniques for encoding inputs (images, texts).

They do mention mesh exports (there has been some research into polygon generation from GS).

If the system scales to huge worlds this could change game-dev, and there seems to be some aim with the control methods, but it'd probably require more control and world/asset management since you need predictability with existing things to produce in the long term (same as with code agents).

Along with entertainment, they can be used for simulation training for robots. And allow for imagining potential trajectories

Other "world model"s are Image + (keyboard input) to Video or Streaming Images, that effectively function like a game engine / video hybrid.