Synthesis AI offers a synthetic data generation platform specifically designed for computer vision applications, enabling the creation of privacy-compliant and unbiased datasets. This technology addresses the need for high-quality training data in areas such as biometric identification, autonomous vehicle behavior simulation, and augmented reality, facilitating faster model development and deployment.
Funding
$25M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Developing computer vision models requires large, high-quality datasets, but acquiring real-world data can be expensive, time-consuming, biased, and raise privacy concerns. Capturing edge cases and rare events is particularly challenging and often impossible with real data alone.
Solution
Synthesis AI provides a synthetic data generation platform that addresses the limitations of real-world data for training computer vision and perception AI. The platform enables the creation of privacy-compliant, perfectly labeled, and unbiased datasets tailored for specific applications. By simulating diverse scenarios and edge cases, the platform allows developers to augment real data and ensure comprehensive coverage of critical use cases. This approach facilitates faster model development, reduces bias, and enhances the performance and safety of computer vision systems.
Target Audience
The primary target audience includes computer vision engineers, machine learning researchers, and AI developers in industries such as biometrics, security, consumer electronics, augmented reality, virtual reality, and automotive.
Features
- Generation of synthetic datasets for various computer vision applications, including biometrics, security, consumer devices, AR/VR/XR, and automotive.
- Ability to simulate edge cases and rare events to improve model robustness.
- Creation of diverse and balanced human datasets to mitigate bias in AI models.
- Pixel-perfect 3D labels, including depth, surface normals, and 3D landmarks.
- Customizable parameters for controlling body type, pose, clothing options, environments, and camera types.
- Support for multi-person scenarios and activity recognition.
- Data generation for driver monitoring and pedestrian detection in complex environments.