Qualitest offers end-to-end synthetic data generation services tailored for AI model training and simulation. Our GenAI Data Services pipeline covers everything from 3D asset development to post-processing and validation, ensuring scalable, high-quality datasets that reflect real-world complexity.
We incorporate procedural generation to simulate rare and edge-case scenarios, use LLM-based text and dialogue generation for language-rich environments, and leverage GANs and simulators to create highly realistic visual and 3D data.
3D Asset Creation
Synthetic datasets begin with lifelike 3D models.
These assets include:
- Characters
- Environments
- Digital Doubles or Twins
- Vehicles
3D assets are created using industry-standard tools such as Maya, Blender, 3DS Max, Houdini, and ZBrush, combined with advanced photo manipulation and texture software like Photoshop, Substance, and Marmoset Toolbag.
Assets can be:
- Built from scratch
- Generated using existing models
- Developed via photogrammetry techniques
Scenario Data Creation
- Semantic Labeling
Labels and metadata are added to 3D models, either manually or through automated Python scripts. Proper segmentation ensures that parts of the model are distinguishable and traceable through the pipeline. - Simulations
Physics-based animations simulate real-world interactions, enriching the data with natural movement and environmental factors.
- Variation at Scale
Procedural techniques and scripting are used to introduce large-scale variations, including geometry, textures, and lighting, critical for reducing data bias. - Sensor Modeling
Synthetic datasets require camera configurations that match real-world devices. We replicate settings like resolution, aperture, field of view, and depth of field to ensure realistic sensor data capture.
Data Rendering & Processing
- Pipeline Workflow
We integrate prepared 3D assets into rendering engines while ensuring metadata and auxiliary outputs (AOVs) like depth, segmentation, and ID passes are correctly configured. - Data Rendering
Assets are rendered through the designated engine, converting virtual scenarios into image or video datasets. - Post Processing
Rendered data undergoes enhancements like:- Background compositing
- Motion blur and image effects
- Contrast and color correction
- Frame adjustments, all executed via automated scriptin
- אימות נתונים
Quality assurance is crucial. Validation involves:- Visual inspection of frames
- Metadata verification
- Semantic label checks
- Ensuring compatibility with the final training pipeline
With Qualitest’s synthetic GenAI data services, you can create diverse, high-fidelity datasets that scale, improving the robustness of your AI and ML models.
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