Objaverse Alternatives: Why Objaverse Assets Don't Simulate

Key takeaways

• Objaverse is a free library of 3D models built to teach AI to generate shapes. It is very good at that. Simulation is a different job, with different requirements.
• A simulator needs more than a shape. It needs to know how heavy an object is, how slippery, and where its edges stop. Objaverse files carry none of that.
• You get no error message. The simulator quietly invents the missing numbers, so your robot trains against objects that weigh the wrong amount and never slip.
•  Filtering does not fix it. You can delete the bad files. You cannot add a weight nobody ever measured.
• Physicl is the alternative: objects that simulate the moment they load, in real-world units, with weight, grip and sensor-accurate surfaces, labels included, and clean rights for commercial use.

You found Objaverse the way everyone does. It is free, it is enormous, and it installs in one line.

The files open in Blender and look exactly right. Then you load them into a simulator to train a robot, and the scene falls apart. A chair the size of a building. A mug lying on its side. A cup that drops through the table, and a shelf that explodes the first time anything touches it.

Nothing is broken. The files are fine and the simulator is fine. They were built for different jobs, and this is where that shows.

Physicl is the data layer for Physical AI: simulation-ready 3D assets, built for training rather than rendering. The private beta will open soon. Request access and get objects that simulate on arrival.

What Objaverse Is

Objaverse is the largest free collection of 3D models in the world, and it changed what 3D research could attempt. Any honest comparison starts there.

It came out of the Allen Institute for AI in 2022 as a set of 818,000 models made by 160,000 artists, gathered from Sketchfab, where creators had published them under Creative Commons licences. A second release, Objaverse-XL, grew it to around ten million by adding models scraped from GitHub, Thingiverse, Polycam and the Smithsonian's public archive. For scale, the dataset it replaced held fifty thousand.

It was built for one purpose: teaching AI to generate 3D shapes. Everything about how it was made points that way. The objects were rendered from random angles and scored on how good the pictures looked. The team's own stated worry, in the paper, is that ten million is still small compared with the image datasets used to train picture generators.

That is exactly the right concern for a shape generator. It is the wrong dataset for a simulator, and its authors never claimed otherwise. The mismatch only shows up when you ask it to do the other job.

What a Simulator Needs That a 3D Model Doesn't Have

A rendering engine asks one question of a file: what does this look like? A physics engine asks a completely different one: what does this do when something pushes it?

The second question needs information the first has no use for, so 3D files built for rendering simply do not store it. That is not a flaw in the format. Nobody rendering a film needs to know how much a chair weighs.

It is worth being specific about what "simulation-ready" means, and the clearest definition is NVIDIA's, because NVIDIA builds Isaac Sim, one of the most widely used robotics simulators. Their SimReady standard sets out what an object must carry before it can be used:

• A collision shape, so the simulator knows where the object begins and ends.
•  Weight and balance, so pushing it produces the right result.
• Friction and bounce, so it grips, slides or rebounds like the real thing.
• Joints, for anything with a lid, a hinge or a drawer.
• Labels, so the scene can double as training data.

NVIDIA is blunt about what usually turns up instead. Most 3D content arrives without any of it, which makes it "unreliable for physical AI workflows".

A file downloaded from Objaverse carries none of those properties. Here is what that means in practice.

No Weight, No Grip, No Edges

Three pieces of information decide whether a simulated object behaves like a real one.

• Weight and balance. A full kettle and an empty cardboard box can be identical shapes and behave nothing alike. A 3D model stores neither the mass nor where that mass sits.
• Grip. Friction decides whether a gripper holds a jar or drops it, and whether a box slides off a moving pallet. It is a property of the material, and a 3D file describes materials only visually: colour, shininess, a texture image. A surface can look like brushed steel and carry no information about how steel behaves.
• Where the object stops. This one surprises people. A simulator does not use the detailed mesh you can see; it uses a simplified shape for working out collisions. MuJoCo, one of the standard robotics simulators, shrink-wraps any hollow object into a solid outline. A mug becomes a lump. A robot can no longer put anything in it, and no longer reach through the handle.

The dangerous part is that none of this throws an error. With no weight given, a simulator works one out by assuming the object is solid all the way through. With no friction given, it applies a default. The scene runs, the numbers look plausible, and you find out the training was wrong when the robot meets the real object.

Even the Objaverse team ran into this. To use their own models in a simulated house, they had to rebuild collision shapes and cut every mesh down to five thousand vertices first.

Nothing Is Life-Sized, and Nothing Faces the Same Way

There is a simpler problem sitting underneath. The models are not to scale and not consistently oriented, because neither mattered to the artists who made them.

A team building a driving simulator from Objaverse put it plainly: the assets "often have non-metric scales, with examples such as a cucumber being as large as a car". Orientation is the same story. Nothing guarantees the chair faces forward or the mug sits upright, and the file format Objaverse ships in disagrees with most robotics simulators about which direction counts as up.

Neither is hard to fix on one object. Both are weeks of work across a hundred thousand.

Why Filtering Objaverse Doesn't Fix It

The obvious answer is to keep the good files and throw the rest away. Plenty of teams have tried, and they all report much the same thing.

The driving-simulator team above started with 800,000 Objaverse models and kept 158,000. Reaching that figure took ten annotators three weeks just to sort usable from unusable. Adding the physical properties came afterwards. Other teams filtering for simulation report the same pattern: most of the library does not survive, and much of what gets cut isn't even a single object. A separate study that checked a sample of 100,000 found four in ten entries are whole scenes rather than individual things, and a room cannot be picked up.

But the retention rate is not really the point. Filtering is subtraction. You can remove every model that is unusable and you still have not added a single gram of weight, because nobody ever measured it. Whatever survives your filter still needs scale, orientation, collision shapes, mass and friction attached by hand.

And the work does not finish. A filter tuned for street furniture returns nothing useful for a warehouse. Every new environment starts the sorting again. A team that goes this way has not bought a library. It has taken on a permanent job.

The Rights Question

There is a second problem that stops commercial deals rather than training runs.

Objaverse is free, but "free" and "yours to use" are different things. The dataset is published under an open licence that covers the list of models, not the models themselves. Each object keeps whatever licence its original creator chose, and the authors say plainly that working out what you are allowed to do with each one is the downloader's job.

For the original release, the published breakdown lists 25,000 objects as NonCommercial, 52,000 as NonCommercial with share-alike, and 16,000 as share-alike alone. The first two groups cannot go into a product you sell. Share-alike can attach conditions to whatever you build from the rest. Those you can at least identify and remove, because the licence is recorded.

The harder problem is the part of Objaverse-XL scraped from GitHub and Thingiverse, which is most of it. Files there were collected by file extension, and no licence analysis has been published for either source. That is a large hole to hand to a legal team, and we have written separately on the rights problem behind training data.

Physicl: The Alternative Built to Simulate

Physicl starts from the other end. Rather than collecting models and filtering toward a standard, every object is built to the standard from the start.

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Most of that table is work you could do yourself, given time. Two rows are not, because they have to be captured when the object is built.

• Sensor behaviour. Physicl describes every surface by what it is made of, so one object answers a camera, a radar and a LiDAR correctly. A downloaded file cannot be corrected into this, because nobody measured its surfaces in the first place.
• Labels. The answer key is produced with the scene, not after it. And since scenes are built by code, one environment becomes thousands of labelled variants. Filter a free library and you still have to pay someone to label it.

That is the difference between generating training data and collecting it.

Other Libraries, and Where They Stop

Physicl is not the only alternative to Objaverse, and it is worth knowing what the others trade away.

• ShapeNet is the older academic standard: clean, well organised, research use only, and visibly dated.
• Google Scanned Objects is 1,030 scanned household items under CC-BY. Clean and usable, but far too small to train on.
• Amazon Berkeley Objects is around 8,000 models carrying real dimensions and weights, which is rare and genuinely useful. The licence is non-commercial, so it cannot go into a product you sell.
• PartNet-Mobility is the place to look if you need objects with working hinges and drawers. It is an academic release built on the ShapeNet lineage, so check the terms before using it commercially.
• Infinigen generates everything procedurally, which removes the rights question entirely. It started with natural environments and has since added interiors.
• NVIDIA's own SimReady packs are correct by definition, since NVIDIA wrote the standard. They run to roughly a thousand objects across five packs, almost all of them warehouse, furniture and shipping.

The pattern is consistent. You can have scale, or physics, or a clean commercial licence. Nothing free gives you all three, and no amount of filtering adds a property that was never captured in the first place.

The Real Choice

This is not a contest between two libraries of shapes. On shapes, Objaverse wins easily, and if you are training something to generate 3D models, use it.

The real question is whether you want to spend the next year building the missing layer yourself. Physical AI is held back by data, not models. Objaverse proved that 3D content can be gathered at enormous scale. The unsolved part is describing it accurately enough for a physics engine to act on. That is infrastructure. It should be built once, not rebuilt by every robotics team in turn.

Request access to the Private Beta, or talk to us about a simulation-ready library for your environment.

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