NVIDIA’s new AI model quickly generates objects and characters for virtual worlds

wants to take the sting out of creating 3D virtual worlds with a new model. GET3D can generate characters, buildings, vehicles, and other types of 3D objects, says NVIDIA. The model should also be able to cast shapes quickly. The company notes that GET3D can generate around 20 objects per second with a single GPU.

The researchers trained the model using synthetic 2D images of 3D shapes taken from multiple angles. NVIDIA says it took just two days to feed around 1 million images into GET3D using A100 Tensor Core GPUs.

The model can create objects with “high-fidelity textures and complex geometric detail,” according to NVIDIA’s Isha Salian. The molds GET3D makes “are in the shape of a triangle mesh, like a papier-mâché model, covered with a textured material,” added Salian.

Users should be able to quickly import the objects into game engines, 3D modelers and movie renderers for editing as GET3D creates them in compatible formats. That means it could be a lot easier for developers to create dense virtual worlds for games and the Metaverse. NVIDIA named robotics and architecture as other use cases.

The company said that based on a training data set of car images, GET3D was able to generate sedans, trucks, race cars and vans. It can also produce foxes, rhinos, horses and bears after being trained with animal imagery. As you might expect, NVIDIA notes that the larger and more diverse the training set fed into GET3D, “the more diverse and detailed the output.”

Using another NVIDIA AI tool, it’s possible to apply different styles to an object with text-based prompts. They can give a car a burned-out look, turn a model of a house into a haunted house, or, as a video showing the technology shows, add tiger stripes to any animal.

The NVIDIA research team that developed GET3D believes future versions could be trained using real images rather than synthetic data. It may also be possible to train the model on different types of 3D shapes at the same time instead of having to focus on one category of objects at a time.

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Russell Falcon

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