3D Printing Processes

NVIDIA announces ‘LATTE3D’ text-to-3D generative AI model

A reliable text-to-3D-printable file offering seems to be on the horizon

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NVIDIA has announced ‘LATTE3D’, its latest text-to-3D generative AI model – turning text prompts into 3D representations of objects and animals within a second, ‘like a virtual 3D printer’. Crafted in a popular format used for standard rendering applications, the generated shapes can be easily used in virtual environments for developing video games, ad campaigns, design projects, or virtual training grounds for robotics. Considering the trajectory, a reliable text-to-3D-printable file offering is on the horizon.

“A year ago, it took an hour for AI models to generate 3D visuals of this quality – and the current state of the art is now around 10 to 12 seconds,” said Sanja Fidler, Vice President of AI research at NVIDIA. “We can now produce results an order of magnitude faster, putting near-real-time text-to-3D generation within reach for creators across industries.”

NVIDIA announces 'LATTE3D' text-to-3D generative AI model. A reliable text-to-3D-printable file offering seems to be on the horizon.

This advancement means that LATTE3D can produce 3D shapes nearly instantly when running inference on a single GPU, such as the NVIDIA RTX A6000, which was used for the NVIDIA Research demo.

Instead of starting a design from scratch or combing through a 3D asset library, a creator could use LATTE3D to generate detailed objects nearly instantly. The model generates a few different 3D shape options based on each text prompt. Selected objects can be optimized for higher quality within a few minutes. Then, users can export the shape into graphics software applications or platforms such as NVIDIA Omniverse, which enables Universal Scene Description (OpenUSD)-based 3D workflows and applications.

NVIDIA announces 'LATTE3D' text-to-3D generative AI model. A reliable text-to-3D-printable file offering seems to be on the horizon.

While the researchers trained LATTE3D on two specific datasets – animals and everyday objects – developers could use the same model architecture to train the AI on other data types.

If trained on a dataset of 3D plants, for example, a version of LATTE3D could help a landscape designer quickly fill out a garden rendering with trees, flowering bushes, and succulents, while brainstorming with a client. If trained on household objects, the model could generate items to fill in 3D simulations of homes, which developers could use to train personal assistant robots before they’re tested and deployed in the real world.

LATTE3D was trained using NVIDIA A100 Tensor Core GPUs. In addition to 3D shapes, the model was trained on diverse text prompts generated using ChatGPT to improve the model’s ability to handle the various phrases a user might come up with to describe a particular 3D object – for example, understanding that prompts featuring various canine species should all generate doglike shapes.

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Edward Wakefield

Edward is a freelance writer and additive manufacturing enthusiast looking to make AM more accessible and understandable.

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