also check out their interactive examples on the webapp. It's a bit more rough around the edges but shows real user input/output. Arguably such examples could be pushed further to better quality output.
I like that they distinguish between the collider mesh (lower poly) and the detailed mesh (higher poly).
As a game developer I'm looking for:
• Export low-poly triangle mesh (ideally OBJ or FBX format — something fairly generic, nothing too fancy)
• Export texture map
• Export normals
• Bonus: export the scene as "de-structured" objects (e.g. instead of a giant world mesh with everything baked into it, separate exports for foreground and background objects to make it more game engine-ready.
Gaussian splats are awesome, but not critical for my current renderers. Cool to have though.
From what I can tell, you can actually export a mesh in (paid) Marble, whereas I haven't seen mesh exports offered in Genie 3 yet (could be wrong though).
Genie delivers on-the-fly generated video that responds to user inputs in real time.
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.
Is Marble's definition of a "world model" the same as Yann LeCun's definition of a world model? And is that the same as Genie's definition of a world model?
> I work in AI and, to this day, I don't know what they mean by “world” in “world model”.
I have a PhD in ML and a B.S. in physics. What people in ML call a "world model" seems incredibly strange to me. With my physics hat on, a "world model" is pretty clear. It is "a physics." Mind you, there is not one physics, there are competing models and we're just at a point of time that models have converged up to quantum and gravity.
But "a physics" can be a model that describes any world, not just the one we live in. For ML models, this should be based on the data they're processing. Ideally we'd want this to be similar to our own, but if it is modeling a "world" where pi=3, then that's still "a physics".
The key points here are that a physics is a counterfactual description of the environment. You have to have language to formalize relationships between objects. In standard physics (and most of science) we use math[0], though we use several languages (different algebras, different groups, different branches, etc) of math to describe different phenomena. But the point is that an equation is designed to be the maximum compression of that description. I don't really care if you use numbers or symbols, what matters is if you have counterfactual, testable, consistent, and concise descriptions of "the world".
Oddly enough, there are a lot of physicists and former physicists that work in ML but it is fairly uncommon for them to be working on "world modeling." I can tell you from my own experience talking to people who research world models that they respond to my concerns as "we just care if it works" as if that is also not my primary concern. Who the fuck isn't concerned with that? Philosophers?
[0] It can be easy to read "The Unreasonable Effectiveness of Mathematics in the Natural Sciences" out of context as we're >60 years past where math has been the lingua Franca of science. But math is a language, we invented it, and it should be surprising that this language is so powerful that we can work out the motion of the stars from a piece of paper. Math is really the closest thing we have to a magical language https://web.archive.org/web/20210212111540/http://www.dartmo...
Yeh I still don't think there's a fixed definition of what a world model is or in what modality it will emerge. I'm unconvinced it will emerge as a satisfying 3d game-like first-person walkthrough.
As someone with barebones understanding of "world models," how does this differ from sophisticated game engines that generate three-dimensional worlds? Is it simply the adaptation of transformer architecture in generating the 3-D world v/s using a static/predictable script as in game engines (learned dynamics vs deterministic simulation mimicking 'generation')? Would love an explanation from SMEs.
Games are still mostly polygon based due to tooling (Even Unreal Nanite is a special variation of handling polygons), some engines have tried voxels (Teardown, Minecraft genererates polygons and would fall in the previous category as far as rendering goes) or even implict surface modes by composing SDF'y primitives (Dreams on Playstation and more recently unbound.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).
Whenever I see these and play with models like this (and the demos on this page), the movement in the world always feel like a dolly zoom. Things in the distance tend to stay in the distance, even as the camera moves in that direction, and only the local area changes features.
An established founder makes claims X is the new frontier. X receives hundreds of millions in funding. Other less established founders claim they are working on X too. VCs suffering from terminal FOMO pump billions more into X. X becomes the next frontier. The previous frontiers are promptly forgotten about.
I think it's a bit confusing when it comes to terminology, this seems more graphics focused while I suspect that a 10 year plan as mentioned by YLC probably revolves around re-architecting AI systems to be less reliant on LLM style nets/refinements and better understand the world in a way that isn't as prone to hallucinations.
Incredibly disappointing release, especially for a company with so much talent and capital.
Looking at the worlds generated here https://marble.worldlabs.ai/ it looks a lot more like they are just doing image generation for a multiview stereo 360 panoramas and then reprojecting that into space. The generations exhibit all the same image artifacts that come from this type of scanning/reconstruction work, all the same data shadow artifacts, etc.
This is more of a glorified image generator, a far cry from a "world model".
To be fair, multiview-consistent diffusion is extremely hard - it's an accomplishment of it's own right to get right, and still very useful. "World model" is probably a misnomer though (what even is a world model?). Their recent work on frame gen models is probably a bit closer to an actual world model in the traditional sense (https://www.worldlabs.ai/blog/rtfm).
If biology operated this way animals would never have evolved. This is bunk, it has nothing to do with intelligence and everything to do with hyping the oxymoronic/paradox branded as spatial intelligence. It's a flimsy way of using binary to burn energy.
Senses do not represent, if we needed them to in order to survive, we'd be dead before we never saw that branch, or that step, etc. This is the same mistaken approach cog-sci took in inventing the mind from the brain.
The problem is the whole brain prior to sensory and emotional integration is deeply involved so that an incredibly shallow oscillatory requirement fits atop the depth of long-term path-integration, memory consolidation involving allo and egocentric references of space and time, these are then correlated in affinities by sense emotion relays or values. None of this is here, it's all discarded for junk volumes made arbitrary, whereas the spaces here are immeasurably specific, fused by Sharp Wave Ripples.
There's no world model in the instantaneous shallow optic flow response (knowing when to slam on brakes, or pull in for a dive) and in the deep entanglements of memory and imagination and creativity.
This is one-size fits all nonsense. It's junk. It can't hope to resolve the space time requirements of shallow and deep experiences.
Counterpoint: Humans visualize stuff in their minds before trying new things or when learning new concepts. An AI system with LLM based language center and a world model to visualize during training and inference would help it overlap more of human intelligence. Also it looks cool.
Edit: After seeing your edited (longer) comment, I have no idea what you’re talking about.
I'm floored. Incredible work.
also check out their interactive examples on the webapp. It's a bit more rough around the edges but shows real user input/output. Arguably such examples could be pushed further to better quality output.
e.g. https://marble.worldlabs.ai/world/b75af78a-b040-4415-9f42-6d...
e.g. https://marble.worldlabs.ai/world/cbd8d6fb-4511-4d2c-a941-f4...
I like that they distinguish between the collider mesh (lower poly) and the detailed mesh (higher poly).
As a game developer I'm looking for:
• Export low-poly triangle mesh (ideally OBJ or FBX format — something fairly generic, nothing too fancy) • Export texture map • Export normals • Bonus: export the scene as "de-structured" objects (e.g. instead of a giant world mesh with everything baked into it, separate exports for foreground and background objects to make it more game engine-ready.
Gaussian splats are awesome, but not critical for my current renderers. Cool to have though.
I understand that DeepMind is working on this too: https://deepmind.google/blog/genie-3-a-new-frontier-for-worl...
I wonder how their approaches and results compare?
From what I can tell, you can actually export a mesh in (paid) Marble, whereas I haven't seen mesh exports offered in Genie 3 yet (could be wrong though).
Genie delivers on-the-fly generated video that responds to user inputs in real time.
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/
Is Marble's definition of a "world model" the same as Yann LeCun's definition of a world model? And is that the same as Genie's definition of a world model?
Isn't this a Gaussian Splat model?
I work in AI and, to this day, I don't know what they mean by “world” in “world model”.
But "a physics" can be a model that describes any world, not just the one we live in. For ML models, this should be based on the data they're processing. Ideally we'd want this to be similar to our own, but if it is modeling a "world" where pi=3, then that's still "a physics".
The key points here are that a physics is a counterfactual description of the environment. You have to have language to formalize relationships between objects. In standard physics (and most of science) we use math[0], though we use several languages (different algebras, different groups, different branches, etc) of math to describe different phenomena. But the point is that an equation is designed to be the maximum compression of that description. I don't really care if you use numbers or symbols, what matters is if you have counterfactual, testable, consistent, and concise descriptions of "the world".
Oddly enough, there are a lot of physicists and former physicists that work in ML but it is fairly uncommon for them to be working on "world modeling." I can tell you from my own experience talking to people who research world models that they respond to my concerns as "we just care if it works" as if that is also not my primary concern. Who the fuck isn't concerned with that? Philosophers?
[0] It can be easy to read "The Unreasonable Effectiveness of Mathematics in the Natural Sciences" out of context as we're >60 years past where math has been the lingua Franca of science. But math is a language, we invented it, and it should be surprising that this language is so powerful that we can work out the motion of the stars from a piece of paper. Math is really the closest thing we have to a magical language https://web.archive.org/web/20210212111540/http://www.dartmo...
Yeh I still don't think there's a fixed definition of what a world model is or in what modality it will emerge. I'm unconvinced it will emerge as a satisfying 3d game-like first-person walkthrough.
I think absolutely it will in a year
but it sounds cool
What happens when you prompt one of these kind of models with de_dust? Will it autocomplete the rest of the map?
edit: Just tried it and it doesn't, but it does a good job of creating something like a CS map.
>What happens when you prompt one of these kind of models with de_dust?
Presumably de_dust2
You both should check out DiamondWM. It runs on Ubuntu and I think Windows, presuming you have an Nvidia GPU. It's exactly what you're talking about.
I linked it elsewhere in this thread.
As someone with barebones understanding of "world models," how does this differ from sophisticated game engines that generate three-dimensional worlds? Is it simply the adaptation of transformer architecture in generating the 3-D world v/s using a static/predictable script as in game engines (learned dynamics vs deterministic simulation mimicking 'generation')? Would love an explanation from SMEs.
Games are still mostly polygon based due to tooling (Even Unreal Nanite is a special variation of handling polygons), some engines have tried voxels (Teardown, Minecraft genererates polygons and would fall in the previous category as far as rendering goes) or even implict surface modes by composing SDF'y primitives (Dreams on Playstation and more recently unbound.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).
The model is predicting what the state of the world would look like after a given action.
Along with entertainment, they can be used for simulation training for robots. And allow for imagining potential trajectories
Marble is not that type of world model. It generates static Gaussian Splat assets that you can render using 3D libraries.
Whenever I see these and play with models like this (and the demos on this page), the movement in the world always feel like a dolly zoom. Things in the distance tend to stay in the distance, even as the camera moves in that direction, and only the local area changes features.
[0] https://en.wikipedia.org/wiki/Dolly_zoom
This "world model" is Image to Gaussian Splat. This is a static render that a web-based Gaussian Splat viewer then renders.
Other "world model"s are Image + (keyboard input) to Video or Streaming Images, that effectively function like a game engine / video hybrid.
This seems very interesting. Timely, given that Yann LeCun's vision also seems to align with world models being the next frontier: https://news.ycombinator.com/item?id=45897271
An established founder makes claims X is the new frontier. X receives hundreds of millions in funding. Other less established founders claim they are working on X too. VCs suffering from terminal FOMO pump billions more into X. X becomes the next frontier. The previous frontiers are promptly forgotten about.
What’s it going to do, take away funds from the otherwise extremely prudent AI sector?
I think it's a bit confusing when it comes to terminology, this seems more graphics focused while I suspect that a 10 year plan as mentioned by YLC probably revolves around re-architecting AI systems to be less reliant on LLM style nets/refinements and better understand the world in a way that isn't as prone to hallucinations.
Duplicate: https://news.ycombinator.com/item?id=45902732
Incredibly disappointing release, especially for a company with so much talent and capital.
Looking at the worlds generated here https://marble.worldlabs.ai/ it looks a lot more like they are just doing image generation for a multiview stereo 360 panoramas and then reprojecting that into space. The generations exhibit all the same image artifacts that come from this type of scanning/reconstruction work, all the same data shadow artifacts, etc.
This is more of a glorified image generator, a far cry from a "world model".
To be fair, multiview-consistent diffusion is extremely hard - it's an accomplishment of it's own right to get right, and still very useful. "World model" is probably a misnomer though (what even is a world model?). Their recent work on frame gen models is probably a bit closer to an actual world model in the traditional sense (https://www.worldlabs.ai/blog/rtfm).
exciting towards world intelligence
RIP GTA6
Impressive!
If biology operated this way animals would never have evolved. This is bunk, it has nothing to do with intelligence and everything to do with hyping the oxymoronic/paradox branded as spatial intelligence. It's a flimsy way of using binary to burn energy.
Senses do not represent, if we needed them to in order to survive, we'd be dead before we never saw that branch, or that step, etc. This is the same mistaken approach cog-sci took in inventing the mind from the brain.
The problem is the whole brain prior to sensory and emotional integration is deeply involved so that an incredibly shallow oscillatory requirement fits atop the depth of long-term path-integration, memory consolidation involving allo and egocentric references of space and time, these are then correlated in affinities by sense emotion relays or values. None of this is here, it's all discarded for junk volumes made arbitrary, whereas the spaces here are immeasurably specific, fused by Sharp Wave Ripples.
There's no world model in the instantaneous shallow optic flow response (knowing when to slam on brakes, or pull in for a dive) and in the deep entanglements of memory and imagination and creativity.
This is one-size fits all nonsense. It's junk. It can't hope to resolve the space time requirements of shallow and deep experiences.
Counterpoint: Humans visualize stuff in their minds before trying new things or when learning new concepts. An AI system with LLM based language center and a world model to visualize during training and inference would help it overlap more of human intelligence. Also it looks cool.
Edit: After seeing your edited (longer) comment, I have no idea what you’re talking about.
It’s irrelevant - it has nothing analogous to mental imagery. It’s pseudoscience.
Edit - of course you have no idea, you have no grasp of the oscillatory-dynamic origins of consciousness, nor does it seem anyone in AI.
I don't know who you are or why you're so negative, but I have an immediate use case for this and I'll come back and post a "Show HN" on this.
This is immensely useful tech for blocking out consistent scenes, eg. for video generation.
Beyond entertainment, this is going to be huge in the field of robotics.
I wish you could see what I see.
This is arbitrary, it has nothing to do with specifics or coherence. Study it carefully particularly as it relates to general robitics.
I’m really curious to see what you are cooking. What field are you in?