HELIOS
Since 2021, AI-Verse has helped computer vision teams eliminate the data bottleneck that slows model development. Our proprietary procedural engine powering Helios, Gaia for outdoor scenes, and specialized solutions for smart home and security & surveillance generates photorealistic labeled datasets without a single real-world image. Independent benchmarks show that models trained exclusively on high-quality synthetic data can match or exceed the accuracy of real-data baselines when scene diversity is sufficiently controlled exactly what Helios’s 50+ parameter system is designed to deliver.
With our procedural engine, simply set your desired parameters and watch as Helios generates the scene for you.
Generate your own custom datasets on-demand. Save time and resources while gaining full control over scene configurations for AI training.
Each image in your dataset comes fully labeled. Enjoy pixel-perfect labels without bias, enabling your models to learn from high-quality data that reflects real life.
With more than 50 adjustable parameters, configure scenes according to your specifications from sensor settings and lighting conditions to activities and labels.
Helios allows you to instantly preview images during your session, so you can refine scenes on the fly and get the quality you expect.
1.
Get Started
Create a project and add your first batch. You can add as many batches as you want to each project.
2.
Set the scene
Select the type of environment you need. Add specific objects of interest from a catalog with 3D assets. Your objects of interest are automatically added to each scene.
3.
Call the Action
Select the activities you are interested in. Set various parameters related to the characters you are adding such as age, gender, physical characteristics, ethnicity, etc.
4.
Lights
For each batch, select several lighting scenarios from a catalog including various artificial and natural lighting conditions. You can even simulate pictures taken with a flash if desired.
5.
Camera
Set your camera’s intrinsic and extrinsic parameters to match your use case. For example, simulate images from a fixed surveillance camera, a drone, satellite image.
6.
Generate
Select the labels you need among instance and semantic segmentation, depth image, 3D normal image, albedo image, Lambertian reflectance model, or skeleton key points. Next, choose the number of scenes and images per scene. Then, generate your fully labeled dataset.
Configure the images you need in minutes
Click to generate
Download your automatically labeled dataset
Configure with more than 50 parameters and optimize your dataset.
Image previews are accessible at every step, so you see the results in real-time.
Add various human activities to your scenes. Select the gender, age, ethnicity and physical characteristics of the human participants.
Flexible pay-per-use system. Datasets are stored securely and delivered via the cloud—no minimum or a maximum number of images. Expand your datasets incrementally as needed.
Labels are pixel-perfect and bias-free.
Our data has been successfully used to train models in various areas, including:
Fall Detection
Abandoned Luggage Detection
Weapon Detection
Human Detection
Paris/ Stockholm, June 14, 2026. AI Verse, the French procedural synthetic data platform, and Scaleout, the Swedish sovereign edge AI company today announced a strategic partnership to jointly develop end-to-end machine learning capability for computer vision at the tactical edge. Under the Memorandum of Understanding, the two companies are cooperating on developing AI for defence. […]
Recognizing joint innovation in thermal UAV detection for frontline defense When AI Verse and Soloma Avionics began working together, our shared goal was clear: improve thermal detection performance where it matters most – saving lives in Ukraine. Our partnership has now been recognized by the Defence AI Council as the Finalists for Partnership of the Year award. […]
Defense and drone CV engineers face a persistent issue: field-collected data falls short for robust models, leaving gaps in edge cases like rare weather or occluded targets. No amount of flights or ground tests delivers the volume, diversity, or labels needed for mission-ready detection. Synthetic data addresses this directly by generating precise, scalable datasets that […]
