Proprietary pipeline brings together AI and CGI
3D Models Built Using GANs & CGI
Scene Composition
Image Rendering
Data & Labels
Read our white paper on the
state-of-the-art in synthetic data
Pixel-perfect labels
for complex tasks

Since the image data is generated, the attributes of every pixel are known. Complex images are automatically labeled, enabling you to efficiently and cost-effectively train complex models.

Complicated Semantic Segmentation
Detailed Sub-Segmentation
More robust models through more diverse data
Programmatic control lets you create a high degree of inter-and intra-class variability. Modify geometries, textures, environments, lighting, imaging modality & camera location to generate vast amounts of diverse training data to power higher performing and more generalized computer vision, models.
More robust models through more diverse data
Programmatic control lets you create a high degree of inter-and intra-class variability. Modify geometries, textures, environments, lighting, imaging modality & camera location to generate vast amounts of diverse training data to power higher performing and more generalized computer vision, models.
More robust models through more diverse data
Programmatic control lets you create a high degree of inter-and intra-class variability. Modify geometries, textures, environments, lighting, imaging modality & camera location to generate vast amounts of diverse training data to power higher performing and more generalized computer vision, models.
New labels to create
new models
Human labelers are unable to provide a wide-range of necessary labels for emerging applications. Synthesis’ platform can provide an expanded set of pixel-accurate labels (e.g. depth, 3D segmentation, complex pose, surface normals, material properties, etc) to enable you to build new and more capable models.
Depth
Surface Normals
Edges
3D Segmentation
Complex Object Recognition
Read our white paper with Google Brain Read about our work on the Google AI blog
Build better products more efficiently with virtual prototyping

Model camera placement, image modality, resolution, and many more attributes to inform the overall design of your computer vision systems. Using synthetic data enables you to make smarter choices and to better understand model performance before building and deploying hardware.

Simulate camera type & placement

Synthesis AI Raises a $17 Million Series A To Expand Its Synthetic Data Platform for Computer Vision AI

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