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Roblox Unveils Cube 3D: An Open Source AI Model for 3D Creation

Cube 3D is Roblox's bet on revolutionizing 3D content creation.

roblox logo with an ai banner above it with two floating robots trying to create something on a canvas

Roblox is a popular game platform that has a passionate community of gamers behind it. The platform lets them make, share, and play games, while also giving them a way to make money with tools like Roblox Studio.

Six months after teasing an open source AI 3D world creator, they have now open sourced the first release of the foundational model that makes it all happen.

Roblox Cube 3D: What to Expect?

Named Cube 3D, this foundational AI model can be used to generate 3D objects and environments from simple text prompts. Roblox has showcased a demo (the video above) that shows it in action, effortlessly creating new 3D elements according to prompts.

The basis for this model is a novel 3D tokenization technique spearheaded by Roblox, where 3D objects are represented as tokens. With this approach, complex 3D structures are broken down into smaller, more manageable components, similar to how words are tokenized in natural language processing.

Using these tokens, the model predicts the next 'shape token' in sequence to construct complete 3D objects or even entire scenes. This approach allows Cube 3D to quickly generate functional and structurally accurate 3D models that are fully compatible with game engines.

Roblox describes the 3D generation process like this:

To achieve 3D generation, we designed a unified architecture for autoregressive generation of single object, shape completion, and multiobject/scene layout generation. Autoregressive transformers are neural networks that use previous inputs to predict the next component.
This architecture provides both scalability and multimodal compatibility so that as we expand the model, it will work with many different kinds of input (text, visual, audio, and 3D).

The source for Cube 3D is set to appear on Roblox's GitHub page later this week, if you are interested in learning more about the inner workings of the model, I highly suggest you go through the announcement blog.

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