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Synthetic data use-cases in computer vision

Virtual product
design

By creating digital doubles of complex computer vision product systems, you can perform design trade-off studies in a virtual environment. Optimize camera configurations and understand performance before building hardware.

Simulate edge cases and rare events

Capturing real data of edge cases and rare events is prohibitively expensive and often impossible. Synthetic data can be used to augment real data to ensure coverage of critical use cases that impact system performance and safety.

Reduce bias & preserve privacy

Real-world data contains many biases. Biases related to demographics have significant ethical and legal implications. Synthetic data enables companies to build diverse and balanced human datasets to mitigate bias in a privacy-compliant manner.

Pixel-perfect
3D labels

Spatial computing, autonomy, AR/VR, and robotic applications require detailed knowledge of the 3D world. With synthetic data, developers now have access to pixel-perfect annotations of depth, surface normals, 3D landmarks, and more to build better models.

Synthesis AI supports a broad range of computer vision (CV) applications

ID verification

Millions of images of unique individuals to build privacy-compliant and unbiased facial ID models

Security

Create multi-person scenarios across environments for activity recognition and threat detection.

AR/VR/XR

Human-centric ML models for headset hardware and software development in consumer, industrial, and enterprise applications.

Virtual Try-on

Control over body type, pose, and millions of clothing options to create the most robust models.

Driver monitoring

Model complex driver and occupant behavior across demographics, car interiors, and camera types.

Pedestrian detection

Simulate multi-person scenarios in complex outdoor environments, all with perfect pose and segmentation labels.

ID verification

Millions of images of unique individuals to build privacy-compliant and unbiased facial ID models

Security

Create multi-person scenarios across environments for activity recognition and threat detection.

AR/VR/XR

Human-centric ML models for headset hardware and software development in consumer, industrial, and enterprise applications.

Virtual Try-on

Control over body type, pose, and millions of clothing options to create the most robust models.

Driver monitoring

Model complex driver and occupant behavior across demographics, car interiors, and camera types.

Pedestrian detection

Simulate multi-person scenarios in complex outdoor environments, all with perfect pose and segmentation labels.

We wrote the book on Synthetic Data

“…our field must tap into rich sources of synthetic and real data. Sergey Nikolenko’s book lucidly surveys the state of the art in the former, and I consider it required reading for any researcher using deep learning based methods.”

— SERGE BELONGIE

Department of Computer Science at the University of Copenhagen (DIKU) and Director, Pioneer Centre for Artificial Intelligence

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