google-research/kubric

Health v2 · maintenance only

Adoption not part of the score

2808 stars · 281 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Kubric is a data generation pipeline from Google Research for creating semi-realistic synthetic multi-object videos with rich annotations like instance segmentation masks, depth maps, and optical flow. It is built on top of pybullet for physics simulation and Blender for rendering, with a modular design that can support other rendering backends.

Use cases

• generate synthetic video datasets for training machine learning models
• create annotated videos with instance segmentation masks and depth maps
• benchmark multi-object video understanding systems
• generate physics-based object interaction scenes for research
• produce controlled-complexity datasets for evaluating vision models
• create synthetic training data for optical flow estimation

When to choose

• you need labeled synthetic video data for training or evaluating computer vision models
• you want controllable dataset complexity from toy scenes toward realistic video
• you need automatic ground-truth annotations like segmentation, depth, and flow that are hard to obtain from real footage

When to avoid

• you need fully photorealistic real-world video data
• you want a simple one-off renderer rather than a dataset generation pipeline
• you cannot run Docker or Blender-based rendering infrastructure

Facets

library · maturity active

data-generation simulation image-processing machine-learning machine-learning computer-vision data-science simulation python synthetic-data blender pybullet video-datasets segmentation-masks optical-flow depth-maps scene-rendering docker linux gpu

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markdown · JSON · MCP: product_card(name="google-research/kubric")