Synthetic Data

Accelerating AI and 3D simulation workflows.

What is Synthetic Data?

Training AI models or running large-scale 3D simulations requires carefully labeled and diverse datasets that contain thousands to tens of millions of elements, some of which are beyond the RGB spectrum. Collecting and labeling this data in the real world is time-consuming and prohibitively expensive. This can hinder the development of AI models and slow down the time to solution. 

Synthetic data, generated by algorithms and containing annotated information such as images or text, can be used in conjunction with real-world data to train AI models and create Universal Scene Description (OpenUSD)-based 3D simulations. Synthetic data generation (SDG) can save significant time and greatly reduce costs.

Synthetic Data

The Benefits of Synthetic Data

Cost Savings

Overcome the data gap and reduce the overall cost of acquiring and labeling data required to train AI models.


Address privacy issues and reduce bias by generating diverse datasets to represent the real world.


Create highly accurate, generalized AI models by training with data that includes rare but crucial corner cases that are otherwise impossible to collect.


Generate data that scales with your use case across manufacturing, automotive, robotics, and more.

How Synthetic Data is Being Used

Use synthetic data generation applications on-premises or on Omniverse Cloud.

Industrial Inspection

Synthetic data can be used for training AI models to catch defects early in the manufacturing process.

Image courtesy of Siemens

Robotics Simulation

Synthetic data can be used for training robots to move payloads, improving worker safety and streamlining operations.

Autonomous Vehicles

3D synthetic data can be used to develop and test autonomous vehicle solutions in a simulation environment, reducing testing and training times and lowering costs.

Our Ecosystem Partners

Synthetic Data Companies

Service Delivery Partners

Synthetic Data Applications at NVIDIA

Use synthetic data generation applications on premises or on NVIDIA Omniverse™ Cloud.

Omniverse Replicator

Omniverse Replicator is an open and modular SDK that enables accurate 3D synthetic data generation (SDG) to accelerate the training and performance of AI perception networks.

NVIDIA Isaac Sim

Omniverse Replicator powers the synthetic data generation capabilities in the NVIDIA Isaac Sim™ robotics simulation application and can be used to generate synthetic data specific to training AI-based robots.


The NVIDIA DRIVE Sim™ suite of tools uses Omniverse Replicator to generate synthetic data for autonomous vehicle (AV) perception algorithms.

Sensors of an Autonomous Vehicle

Getting Started with Synthetic Data


Learn How to Accelerate Your AI with Synthetic Data


3D Synthetic Data Generation (SDG)

GTC Sessions

See How Developers are Generating Synthetic Data for Real-World Use Cases

See the Latest Synthetic Data News

Build Omniverse SimReady Assets

Are you a technical artist that already knows 3D scripting behaviors, material creation, and lighting techniques?

Your skills are in demand by large companies paying top dollar trying to catch defective parts, train vehicles safely, track packages, and much more. 

Discover NVIDIA Synthetic Data Research

Learn more about research at NVIDIA and the latest publications on synthetic data in areas such as generative AI, computer vision, and more.  Explore the research out of the NVIDIA Artificial Intelligence Lab lead by Sanja Fidler for the latest in computer vision, machine learning, and computer graphics.

Stay up-to-date on the latest NVIDIA Omniverse news.