Synthesis AI provides simulation and synthetic data for computer vision and perception AI. They offer solutions for biometrics and security, consumer devices, and automotive applications. Their synthetic data helps create privacy-compliant human data, unbiased datasets, and faster production cycles. They focus on applications like ID verification, activity recognition, AR/VR/XR, virtual try-on, driver monitoring, and pedestrian detection.
Synthesis AI
Synthetic data for computer vision and perception AI across various industries.
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What is Synthesis AI?
How to use
Synthesis AI offers synthetic data solutions tailored to specific applications. Users can leverage their platform to generate datasets for training computer vision models in areas like biometrics, consumer devices, and automotive. The platform provides tools and resources to simulate various scenarios and edge cases, ensuring robust model performance.
Core Features
- Synthetic data generation for computer vision
- Simulation of various scenarios and edge cases
- Privacy-compliant human data
- Unbiased datasets
- Pixel-perfect 3D labels
Use Cases
- ID verification using facial identification and verification
- Activity recognition and threat detection in security applications
- Spatial computing, gesture recognition, and gaze estimation for AR/VR/XR headsets
- Virtual try-on applications with diverse identities and clothing options
- Driver monitoring systems simulating driver and occupant behavior
- Pedestrian detection in autonomous vehicles, covering edge cases and rare events
FAQ
What are the primary applications of Synthesis AI's synthetic data?
Synthesis AI's synthetic data is primarily used in biometrics and security, consumer devices and applications, and automotive industries. Specific applications include ID verification, activity recognition, AR/VR/XR development, virtual try-on, driver monitoring, and pedestrian detection.
How does Synthesis AI ensure privacy with its synthetic data?
Synthesis AI creates privacy-compliant human data through simulation, which avoids the use of real-world personal information, thus mitigating privacy risks.
What types of data labels are available with Synthesis AI's synthetic data?
Synthesis AI provides pixel-perfect annotations of depth, surface normals, 3D landmarks, and more, which are essential for spatial computing, autonomy, AR/VR, and robotic applications.
Pricing
Pros & Cons
Pros
- Reduces bias in datasets
- Preserves privacy with synthetic human data
- Enables simulation of rare and edge cases
- Provides perfectly labeled 3D data
- Accelerates production cycles
Cons
- May require expertise to configure simulations effectively
- Synthetic data may not perfectly replicate real-world complexities
- Potential dependence on the accuracy of the simulation models