Unlimited Synthetic Training Data for Computer Vision
When real-life image capture is challenging,
AI Verse Procedural Engines generate synthetic image datasets in hours!
What Are Synthetic Image Datasets for Computer Vision?
Synthetic image datasets are artificially generated, fully-labeled collections of images. Can be built from 3D models, physics-based rendering engines, and procedural algorithms that replicate real-world visual conditions. Unlike real-world data collection, synthetic image datasets can be produced on demand with pixel-perfect annotations, across any object class, lighting condition, environment, or sensor type without cameras, field teams, or labeling contractors.
Why choose Synthetic Training Data for CV?
AI Verse procedural technology ensures the highest quality, unbiased, labeled synthetic datasets that will improve computer vision model’s accuracy
On Demand Synthetic Dataset Generation
Generate the images when you need them.
Customizable Synthetic Datasets for Any Computer Vision Model
Gain complete control over configurations, including scenes, sensors, lighting, activities, labels, and more.
Privacy Compliant Synthetic Data
Eliminate privacy concerns by avoiding the use of real-world data.
How AI Verse Generates Synthetic Training Data
Inside the Procedural Engine: How Synthetic Images Are Generated
AI Verse’s procedural engine eliminates computer vision data bottleneck.
Define your parameters: object classes, environments, lighting, sensor type, weather, viewpoint, etc., and the platform generates fully annotated images in 4 seconds on 1 GPU, at any scale, with pixel-perfect annotation.
PROCEDURAL SCENE GENERATION
Scene Layout: Stochastic Decomposition Trees
3D Standardized Assets Database
3D SCENE
IMAGE
RENDER
Complex Labelling
Materials Database
Light Sources
Virtual Camera Controls and Properties
RGB, Infrared And Pixel-Perfect Labels for Every Computer Vision Model
With AI Verse’s procedural engine, training datasets that once took teams three months to build can now be completed in hours. And unlike real-world data, any scenario; adverse weather, rare object configurations, sensor failures, edge cases; can be generated on demand.
Eliminate the need for slow, costly real-world data collection and annotation
with AI Verse Indoor and Outdoor Procedural Engines:
HELIOS
Procedural Engine That Generates Indoor Synthetic AI-Ready Image Datasets
Access Unlimited Synthetic Image Datasets
to Train Your Computer Vision Models!
GAIA
Procedural Engine to Generate Outdoor Synthetic AI-Ready Image Datasets
Trusted by Computer Vision engineers in Top NATO Companies
Generate Fully Labeled Synthetic Image Datasets with Gaia
and Accelerate Your AI Training!
Scale AI Training and Deployment
Cut Cost & Time on Data Acquisition
Generate one fully labeled image in just 4s!
Enhance AI Model Accuracy
Generate all edge cases to improve your models’ accuracy!
Obtain Pixel-Perfect Labels
8 Annotation Types. Zero Manual Labeling
Accelerate
Time-to-Market
Launch faster than ever before and gain a competitive edge!
Use Cases
Built for Defense, Drone, Smart Home and other CV Applications
Defence & Government Recognition
AI Verse is a NATO DIANA (Defence Innovation Accelerator for the North Atlantic) company, selected to develop dual-use synthetic image dataset technology for Allied defence AI applications. DIANA is the NATO flagship initiative connecting leading deep tech startups with Allied defence programmes. AI Verse also was mentioned in the speech by the President Emmanuel Macron during his speech at Adopt AI, recognizing AI Verse as a national technology champion. The company is backed by Supernova, Innovacomm, and Bpifrance. These credentials reflect AI Verse standing as a trusted provider of physics-based synthetic RGB and infrared training data for defence, aerospace, and drone manufacturers across Europe.
FAQs
What Pixel-Perfect Labels Are Included in AI Verse's Synthetic Image Generation Engine?
There are 8 pixel-perfect labels included: Classes, Instances, Depth, Normals, 2D/3D Bounding Boxes, 2D/3D Keypoints, Skeletons, and Color.
How Does AI Verse Generate Synthetic Data for AI Model Training?
Users select the desired parameters for the environment, scenes, objects, activities, lighting, and more. Based on these criteria, our engine can generate an unlimited number of diverse, varied, and labeled images ready for AI model training.
How Accurate Are the Labels in AI Verse's Synthetic AI Datasets?
Yes, our automated system ensures that each generated image contains 8 pixel-perfect labels, reducing the risk of inaccuracies and guaranteeing the highest data quality.
What Is The Difference Between AI Verse Synthetic Image Data and GenAI Images?
Our proprietary procedural technology generates images based on human input. Users select various criteria for the image from a menu in a step-by-step process, rather than typing a prompt into a GenAI tool. This approach minimizes mistakes and ensures the highest possible realism in our images.
How fast do you generate images?
It takes 4s to generate one labelled image on 1 GPU. Generation can be spread across several GPUs (max 10).
How AI Verse Solves The Domain Gap Problem in Synthetic Data?
The most common objection to synthetic training data is the domain gap: the performance drop that occurs when a model trained on synthetic imagery is deployed against real-world sensor data. For a long time, this objection was valid. Game-engine or GAN-generated images lacked the physical accuracy that defense and industrial CV applications demand.
AI Verse addresses the domain gap through physics-based rendering. Rather than approximating how light and objects appear, the AI Verse procedural engine simulates actual sensor physics: infrared thermal signatures, lens distortion profiles, motion blur at specific shutter speeds, atmospheric scattering across operational distance ranges, and surface material reflectance. The output imagery is not a stylized approximation of reality, but it is a physically accurate simulation of what a specific sensor would capture in a specific environment.
The second mechanism is procedural variation. Every generated dataset draws from a continuous space of randomized scene parameters: object positioning, lighting angle, weather condition, background clutter, and viewpoint. This prevents the overfitting that occurs when synthetic datasets use fixed templates. Models trained on AI Verse data generalize because they have been exposed to the full distribution of conditions they will encounter in deployment, not a curated sample of them.