Unity Computer Vision

Build accurate, production-ready models faster.

Build high-quality synthetic data faster with our expanded suite of open-source tools. View free tools

Free tools and content for generating synthetic data

Check out our newly open sourced and academically released tools, datasets, and dataset generators for the creation of synthetic data for computer vision model training.

Tools and content
Unity Perspective 1.0 featuring grocery products

Unity Perception 1.0

The Perception package provides a toolkit for generating large-scale datasets for computer vision training and validation. Unity Perception 1.0 is a new, more complete release that includes new labels, randomizers, samples, and rendering capabilities.

Use open-source tools
Data analysis and visualization in Unity engine

Data analysis and visualization in Python

PySOLO tools is a new open-source python package that provides utilities to analyze and visualize data in the new SOLO format.

Use open-source tools
SynthHomes demo

Synthetic Homes

Now available as an open-source release, Unity SynthHomes is a 100,000-image dataset of synthetic home interiors and an associated dataset generator binary.

Use open-source content
Synthetic Humans

Synthetic Humans

Now available for academic use only, Unity Synthetic Humans is a 3D person generator built from the ground up for human-centric computer vision.

Use content under academic licensing
Jack Hsu, Senior Manager, Boeing Vancouver

“Wherever there is a requirement for data to drive machine learning, there is a role for synthetic data. Creating synthetic datasets in a virtual world means you can create millions of images very quickly compared to going out to the field and taking pictures.”

Jack Hsu, Senior Manager, Boeing Vancouver
Dogan Demir, CEO, Ouva

“At Ouva, our patient monitoring platform used Unity Computer Vision to generate synthetic data and reduced our month-long live data capture cycles to a week, while our dataset grew by 10X and model accuracies improved by 5 to 10%.”

Dogan Demir, CEO, Ouva

Case studies


Ouva’s simulated healthcare data platform harnesses the power of synthetic data to improve model performance by over 10%, reduce labeling costs by up to $40,000, create balanced datasets in hours instead of weeks, and reduce iteration cycles from weeks to days.


In this interview, learn how Boeing worked with Unity to generate over 100,000 synthetic images to better train the machine learning algorithms of its augmented reality (AR)-powered aircraft inspection application.


Gain insight into how Passio combines Unity’s synthetic data with real-world data to expand its datasets and speed up AI training for AI and augmented reality (AR) applications.

Neural Pocket

Learn how AI startup Neural Pocket used Unity Computer Vision to significantly reduce computer vision model development costs and time to deployment (from 24 weeks to 1 week).

Accelerate computer vision training

Looking for help in synthetic data generation and computer vision? Get expert consulting and professional services from our team of experts.

Frequently asked questions

Synthetic data doesn’t look like real data. Does it really work?

Check out our papers to see how models trained with synthetic and real data outperform models trained using only real data:

What types of computer vision applications can be trained with synthetic data?

Our customers use Unity to generate synthetic data for a variety of computer vision applications, including human detection, object detection, manufacturing defect detection, consumer electronics applications in the home, and more.

When can I use synthetic training data?

You can use synthetic training data when:  

  1. You have only a small sample set of real-world data. In this case you can augment your real-world data with a large amount of synthetic data generated by Unity Computer Vision and boost your model performance.
  2. You are not able to collect the right real-world data for your project. In this case you can use Unity Computer Vision to generate high-quality labeled synthetic images and bootstrap your models with purely synthetic data.

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