UK What synthetic data generation means
Synthetic data generation is the process of creating artificial data for training and testing AI models, rather than collecting it from the real world. The data can be images and video rendered from a 3D scene, readings from a simulated sensor, text written by a language model or rows produced by a statistical model. Done well, it shares the patterns that matter in real data without being copied from it.
For computer vision and robotics, the biggest advantage is the labels. A simulator knows exactly where every object is, so each rendered frame comes with pixel-perfect bounding boxes, segmentation masks, depth and pose for free. Labelling the same frames by hand is slow, expensive and never perfectly consistent.
How synthetic data is generated
Several methods are in use, and many pipelines combine them.
-
//
3D simulation and rendering. A scene is built from 3D models of parts, machines, people and rooms, then rendered from many camera positions. Tools include NVIDIA Omniverse Replicator and Isaac Sim, and game engines such as Unity and Unreal Engine.
-
//
Domain randomisation. Lighting, textures, colours, backgrounds, camera angles and object poses are varied at random across thousands of frames, so the model learns the object rather than the scene. The technique was popularised by robotics research published in 2017.
-
//
Generative models. Generative adversarial networks, diffusion models and world foundation models such as NVIDIA Cosmos create new images or video, or restyle simulated frames to look more like real camera footage.
-
//
Data augmentation. Flips, crops, blur, noise and colour shifts applied to real images. It is not fully synthetic, but it is the cheapest way to stretch a small dataset.
-
//
Rule-based and LLM-generated data. Tabular records from statistical models, and text or instruction data written by large language models for training other language models.
Why synthetic data matters for AI
Real data is often the bottleneck in an AI project. Rare defects may appear once in thousands of parts, and collisions, falls and near misses cannot be staged safely. Footage of people raises privacy and data protection questions. Some classes are badly under-represented, which biases the model. And every real image needs labelling before it is useful.
Synthetic data addresses each of these. You can generate a thousand examples of a scratch that has only been seen twice, render a forklift reversing into a walkway without anyone at risk, balance the classes deliberately and test the model against conditions the site has not yet seen, such as low sun or heavy rain.
Synthetic vs real training data
Neither type is better in every respect. The table shows where each is strong.
| Aspect | Real data | Synthetic data |
|---|---|---|
| Labels | Drawn by hand, can be inconsistent | Generated automatically and exact |
| Cost of more examples | Rises with every image labelled | Low once the scene or model exists |
| Rare and unsafe events | Wait for them to happen | Generate on demand |
| Privacy | May contain personal data | None if no real records are used |
| Realism | Matches the site by definition | Can differ from real cameras and lighting |
| Role in testing | Required for final validation | Useful for stress tests and edge cases |
The sim-to-real gap
A model trained only on rendered images can perform worse on real camera footage, because real sensors add noise, motion blur, lens distortion, reflections and colour casts that a simple render lacks. This is the sim-to-real gap, and it is the main limitation of synthetic data.
Teams close it in four ways: more realistic rendering and camera models, wide domain randomisation, mixing synthetic data with a smaller set of real labelled images, and fine-tuning on real data at the end. Whatever the mix, measure accuracy on a held-out set of real images from the deployed cameras. That is the only result that counts.
Generation and training need GPU servers, often with data centre GPUs. The trained model then runs on edge AI computers beside the cameras, where it has to cope with real footage.
Synthetic data for industrial vision and robotics
Synthetic data is most useful where objects are known in advance, usually from CAD, and real examples are hard to gather.
-
//
Defect inspection. Scratches, dents and missing parts are rendered onto CAD models to train machine vision inspection before a line produces enough real rejects.
-
//
Robot picking. Random piles of parts in bins teach grasp and pose models without photographing every arrangement.
-
//
Mobile robots and vehicles. Warehouses, yards and roads are simulated with people, pallets and weather to train perception and test edge cases.
-
//
Sensor-specific training. Simulated stereo depth and wide-angle lenses help match the training data to the cameras that will be fitted.
Quick answers on synthetic data
Synthetic data: common questions
What is synthetic data?
Artificially generated data. Synthetic data is created by simulation, rules or generative AI rather than measured from real events, and is used to train and test AI models.
Is synthetic data reliable?
When validated against real data. It is reliable for training if the model’s accuracy is confirmed on real images or records. Without that check, a model can learn artefacts of the simulator.
Can you train a model only on synthetic data?
Sometimes, but rarely best. Some detection and pose models work well trained purely on randomised renders. Most teams get better results by adding a smaller set of real labelled data.
What is synthetic test data?
Generated data for testing. In software testing, synthetic test data replaces production records so systems can be tested without exposing real customer information.
Is synthetic data covered by GDPR?
It depends on how it was made. Data generated from scratch with no link to real people is generally not personal data. Data derived from real records can still carry re-identification risk, so check with your data protection lead.
We supply the edge AI computers that run models trained on synthetic and real data, with the GPU or accelerator, camera inputs and operating system image fitted and tested as standard. Send us your cameras and model, and we will match the hardware.