Generative AI for Digital Twins Guide

Generative AI is helping developers who rely on 3D scenes to simplify and accelerate their workflows.

In this workflow guide, we demonstrate how developers can use NVIDIA NIM containers to quickly create and augment complex digital twins for building generative physical AI.

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NVIDIA NIM microservices are a set of accelerated inference microservices that allow developers to easily deploy AI models on NVIDIA GPUs anywhere.

For this workflow, we’ll specifically explore NVIDIA USD NIM microservices that enable developers to take advantage of USD for 3D application and workflow development. These include:

  • USD Code NIM: Utilizes a state-of-the-art large language model that answers OpenUSD knowledge queries and generates USD Python code.

  • USD Search NIM: An AI-powered search for OpenUSD data, 3D models, images, and assets using text or image-based inputs.

Both microservices are currently available as a preview on the NVIDIA API Catalog , where developers can make API calls for evaluation.

You will be able to make your own warehouse dataset and extend the warehouses as you see fit. Here are a few examples from our team to help inspire you to make your own variations:

Follow this step-by-step guide to start your development journey with these new NVIDIA NIM microservices for digital twin and physical AI development!

Workflow Diagram

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➤ Next Steps: Get Started