An ML model is only as good as the data it is trained on, and diverse training data leads to robust production-level models. Our parameterized generators enable complete control over synthetic data distributions, including
body shape, skin tone, lighting conditions, camera angles, and more.
Minimize bias. Ensure privacy. Ship production models with confidence.
All synthetic data comes with pixel-perfect labels that might be hard or even impossible to get via manual annotations. Labels can include semantic segmentation, 2D/3D keypoints, bounding boxes, activity classification, joint angles, depth, surface normals, scene metadata, object attributes (e.g. human body dimensions), and much more.Discover InfiniteRep
Data-centric AI development
Working with synthetic data is an infinitely better experience for ML engineers than collecting and labeling real-world data. Iterate quickly. Debug at the click of a button. And get real results fast. Never be blocked by lack of data again. Synthetic data empowers engineers with the data to solve any ML problem.See how synthetic data is being used
Infinite use cases
World-class ML teams use synthetic data across many application areas.
Synthetic data to train various exercise models from pose estimation to rep counting, form correction to activity classification. Datasets include poor lighting, challenging occlusions, low-contrast clothing, and uncommon camera angles.
Synthetic data to train AR and VR models ranging from hand pose estimation to people segmentation, gesture recognition to natural scene understanding. Datasets include messy backgrounds, grasping objects, complex nails, and diverse wrist-worn accessories from an egocentric or third-person point of view.
Synthetic data to train models related to warehouse safety including PPE detection (hard hats, safety vests, etc), ergonomics feedback, and aisle congestion monitoring. Import real-world objects (e.g. specific PPE or assembly line products) into your simulated dataset.
Synthetic data to train accurate pose estimation and object detection models for automated stores (like AmazonGo). Import specific store layouts, procedurally add inventory to shelves, include diverse shoppers, and generate multi-camera views of a scene.
Synthetic data to train models for residential or enterprise security applications, including package stealing, gun detection, shoplifting, and pet/animal identification.
Want to make synthetic data your competitive edge?
Get in touch with one of our engineers for a scoping session.
Data on demand
Generate data that meets your specs from lighting conditions, camera position, and more. Get started for free. No credit card required.
Our open source datasets are available for both academic and commercial use.
We are leading the charge for synthetic data in computer vision
Published at the NeurIPS
Data-Centric AI Workshop
Introducing InfiniteForm, our synthetic, minimal bias dataset for fitness applications.Read paper