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.
Unsurprisingly the results are by far the best in the area shown in the image in the prompt, and quickly deteriorate beyond it, or more than a couple meters behind the camera.
It's worlds better than just doing gaussian splats from images, but given how much the quality is influenced by images the limit to four images with text prompt or eight images without prompt is quite limiting. That's plenty to describe a chair, but almost nothing to describe a home or a space station. I hope they can extend those limits in future updates
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.
That's the thing about this. Calling things "world models" is only done to confuse people, because "world" is such a loose word. In this scenario the meaning is "3d scene". When others use it, they may mean "screen space physics model". In the context of LLMs it means something like "reasoning about real-world processes outside of text".
Broadly 'world' means 'the domain I'm interested in'. In current use in the DNN context 'world' tends to be physical space at a scale relevant to humans or robots (eg. autonomous vehicles). So when someone says 'world model' you have to ask 'what kind of world, and how is it represented?'.
We don't need to agree on one very specific meaning, which is good, because we would fail.
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.
Aren't the gausian splats the output here? Or are these worlds fully meshed and textured assets?
From my understanding, admittedly quite a shallow look so far, the model generates gaussian splats then from that could implement the collider.
I guess from the splat and the colliders you could generate actual assets that could be interactable/animated/have physics etc. Unsure, exciting space though! I just don't know how I would properly use this in a game, the examples are all quite on-rails and seem to avoid interacting too much with stuff in the environment.
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?
Pretty sure it's used as a marketing term here. They train on images that you generate/give it, but the output of that training is not a model, it's a static 3d scene made up out of gaussian splats. You are not running inference on a model when traversing one of those scenes, you are just rendering the splats.
At the very least it differs greatly from "world model" as understood in earlier robotics and AI research, wherein it referred to a model describing all the details of the world outside the system relevant to the problem at hand.
Very different, it would seem. Then again, it’s never been clear to me why LeCun believes that LLM architectures don’t inherently produce world models in the course of training.
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.
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).
Yeah, I'm likewise a bit underwhelmed by the results.
If you go in with the expectation that you give it a single image and it's doing gaussian splatting from a single image and a prompt it's phenomenal. If you deviate too far from the image viewpoint it breaks down, but it looks decent long enough to be very usable. But if you go in with the expectation that it's generating "worlds" it's not very good. This only passes as a world in a 20 second tech demo where the user isn't given camera controls
My best guess is that they are forced (by investors, lack of investors, fear of the AI bubble, or whatever) to release something, and this was something they could polish up to production quality and host with reasonable GPU resources
Nice tech!
Would be great if this can also work on factual data, like design drawings. With that it could be used for BIM and regulatory use. For example to showcase to residents how a new residential area will look that is planned. Or to test the layout of a planned airport.
Feifei is a great researcher. But to be honest, the progress her company has made in "world modeling" seems to deviate somewhat from what she has advertised, which is a bit disappointing. As this article (https://entropytown.com/articles/2025-11-13-world-model-lecu...) summarizes, she is mainly working on 3DGS applications. The problem is that, despite the substantial funding, this demo video clearly avoids the essentials; the camera movement is merely a panning motion. It's safe to assume that adding even a one-second extra second to each shot would drastically reduce the quality. It offers almost no improvement over the earliest 3DGS demo, let alone the addition of any characters.
This is not a world model, this ise at best the reimplementation of the the NVIDIA prior art around NeRF / 3D Gaussian Splatting and monocular depth, wrapped in a nice product and workflow. What they’re actually shipping is an offline asset generator: you feed it text, images, or video, it runs depth/structure estimation and neural 3D reconstruction, and you get a static splat/mesh world you can then render or simulate in a real engine. That’s useful and impressive engineering, but it’s very different from a proper “world model” in the RL/embodied‑AI sense. Here there’s no online dynamics, no agent loop, and no interactive rollouts; it’s closer to a high‑end NeRF/GS pipeline plus tooling than to something like Google’s Genie/2/3, which actually couples generative rendering with action‑conditioned temporal evolution. Calling this a “world model” feels more like marketing language than a meaningful technical distinction.
Infact my definition of a world model is more closer to what Demis has hinted in his discussions, that video gen models like veo are able to intuit they physics from just video trainingdata suggest that there is an underlying manifold in reality that is essentially computable and thus is being simulated by these models. Building such a model would essentially mean building a physics engine of some kind that predicts this manifold.
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[ 4.4 ms ] story [ 64.0 ms ] threadalso 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...
It's worlds better than just doing gaussian splats from images, but given how much the quality is influenced by images the limit to four images with text prompt or eight images without prompt is quite limiting. That's plenty to describe a chair, but almost nothing to describe a home or a space station. I hope they can extend those limits in future updates
I wonder how their approaches and results compare?
I work in AI and, to this day, I don't know what they mean by “world” in “world model”.
We don't need to agree on one very specific meaning, which is good, because we would fail.
edit: Just tried it and it doesn't, but it does a good job of creating something like a CS map.
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 my understanding, admittedly quite a shallow look so far, the model generates gaussian splats then from that could implement the collider.
I guess from the splat and the colliders you could generate actual assets that could be interactable/animated/have physics etc. Unsure, exciting space though! I just don't know how I would properly use this in a game, the examples are all quite on-rails and seem to avoid interacting too much with stuff in the environment.
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.
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".
If you go in with the expectation that you give it a single image and it's doing gaussian splatting from a single image and a prompt it's phenomenal. If you deviate too far from the image viewpoint it breaks down, but it looks decent long enough to be very usable. But if you go in with the expectation that it's generating "worlds" it's not very good. This only passes as a world in a 20 second tech demo where the user isn't given camera controls
My best guess is that they are forced (by investors, lack of investors, fear of the AI bubble, or whatever) to release something, and this was something they could polish up to production quality and host with reasonable GPU resources
Combine these 2, and we can have moving cameras as needed (long takes). This is going to make storytelling very expressive.
Incredible times! Here's a bet: We'll have a AI superstar (A-list level) in the next 12 months.
Update - yes you can. To be tested.
seems anything to do with asteroids (or explosions I imagine) are blocked.