Synthetic Data Generation

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What Is Synthetic Data?

Training any AI model requires carefully labeled and diverse datasets that contain thousands to tens of millions of elements, some of which are beyond the visual spectrum. Collecting and labeling this data in the real world is time-consuming and expensive. This can hinder the development of AI models and slow down the time to finding a solution. 

Generated by computer simulations, synthetic data is made up of 2D images, 3D data, text, and more, which can be used in conjunction with real-world data to train AI models for computer vision pipelines. Using synthetic data generation (SDG) can save a significant amount of training time and also greatly reduce costs.

Synthetic data

Why Use Synthetic Data?

Cost Savings

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

Privacy and Security

Address privacy issues and reduce bias by generating diverse synthetic 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.

Robotics Simulation

In the field of robotics, synthetic data can be used to train AI models that are deployed for robot perception, grasping, or on robots used for visual inspection.

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Image courtesy of Techman Robot

Industrial Inspection

Detecting defects in manufactured parts is extremely difficult because ‌often the anomalies are subtle. Synthetic data based on actual defects such as scratches, chips, or dents, can be created to train AI models to catch defects early in the manufacturing process.

Image courtesy of Delta Electronics

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Image courtesy of Edge Impulse

Autonomous Vehicles

Deploying an autonomous vehicle so that it can safely navigate its surroundings requires massive amounts of training data, which is extremely expensive and dangerous to acquire in real life. 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.

Generating Synthetic Data

To generate synthetic data, you must first create a digital twin of the environment that you’ll be training your AI model on. 

If training an AI model for a warehouse robot, you will need to create a virtual scene with objects such as pallet jacks and storage racks. If training an AI model for visual inspection on an assembly line, you will need to create a virtual scene with objects such as a conveyor belt and the product being produced.

One of the key challenges that developers face in developing synthetic data pipelines is closing the sim-to-real gap. To create synthetic data that reflects real-world scenarios, you will need to randomize your scene to reflect the plethora of scenarios that an AI model might encounter. This means modifying aspects of the scene such as the position of objects, texture, and lighting. You may also want to modify the camera position and add environmental distractors that may affect the model's performance.

With NVIDIA Omniverse™ Replicator SDK, developers can build custom pipelines that enable technical artists to create and randomize synthetic data for various AI training use cases. Omniverse Replicator powers NVIDIA Isaac Sim™, enabling you to generate synthetic data for robotics applications, and autonomous vehicle simulation, which enables you to generate synthetic data for accelerated development.

Synthetic Data Partner Ecosystem

See how our ecosystem is developing their own synthetic data applications and services based on NVIDIA technologies.

Synthetic Data Companies

Service Delivery Partners


Synthetic Data Training

Take this self-paced course to learn how to generate synthetic data for training computer vision models.

Synthetic Data Documentation

Consult the Omniverse Replicator documentation to get started with synthetic data generation.

Get Started

Build your own synthetic data generation pipeline for robotics simulations, industrial inspection, and autonomous vehicles using Omniverse Cloud APIs or SDKs.