Chaos LunaSynthetic data for vision models

From any input to a digital twin your model can learn from.

Send us photos, video, CAD files or a short brief, or simply tell us what you need. Luna will automatically build a physically accurate digital twin from it. Our 3D team inspects and adds expertise and finishing touches where necessary.

We're open for pilots with teams training vision, diffusion and VLA models.

Input: a photograph of a warehouse Digital twin built from the photograph
Input: one photograph
Rich Library
38,000+

Photoreal Chaos Cosmos assets and materials for your digital twins

World-class Team
180+

Engineers and researchers across R&D and AI/ML

Built on Experience
20+years

Simulating light, optics and material physics

Recognized Craft
Chaos co-founder Vladimir Koylazov holding the Engineering Emmy at the 2021 ceremony

Academy & Emmy

Award-winning rendering technology

Chaos has been honored with both an Academy Award® and an Engineering Emmy® for its role in the widespread adoption of ray-traced rendering in motion pictures and television.
The data problem

Fill the gaps in your training data.

Build hard-to-capture scenarios, generate labels automatically and keep each variation consistent.

Data coverage

Build the edge cases you need.

Vary lighting, objects and camera positions. Introduce defects and interactions. Generate the rare scenarios your model needs to learn.

An isometric warehouse scene with several camera viewpoints and a highlighted carton
Data privacy

Keep training scenes synthetic.

Generate people, environments and movement inside the twin, with complete legal control over every detail in the dataset.

A wireframe person built from editable geometry, surrounded by pose and shape controls
Automatic annotation

Generate precise labels with every frame.

Export boxes, masks and metrics directly from scene geometry, aligned with the rendered output. Correct by construction.

Cartons on a conveyor with precise bounding boxes and depth measurements
Data curation

Keep every dataset consistent.

A shared scene graph keeps labels and object identities aligned across variations and exports.

Aligned image, depth and instance layers connected to one shared scene graph
Digital twins

Build the digital twin once. Produce quality data forever.

A digital twin is a working copy of your environment: real geometry, measured materials, simulated physics and a real camera model. It produces training images with exact labels on every frame.

Inside a Luna twin

One render, many truths.

The robot arm, conveyor and cartons move under simulated physics. Choose a pass to see the ground truth generated with every frame.

Example: robotic pick-and-place cell from an actual YouTube video

Also in every render: surface normals, optical flow and motion vectors, generated from scene geometry and movement.

Photorealistic RGB with bounding boxes for every object.

00:00 / 00:00
Domain randomization

One digital twin.
Thousands of variations.

Change light, atmosphere and camera in the twin and re-render. Every variation keeps its labels.

Digital twin variation
Day / Clear / Wide
Lighting
Atmosphere
Camera

27 combinations
3 controls. 3 choices each.

Real runs also vary materials, objects, physics and more.

Why Chaos

Three things that take decades to build.

Luna runs on Chaos products that are already in production.

Fidelity

  • V-Ray

    Physically based light transport

  • Chaos Scans

    Materials measured from real surfaces

  • Chaos Cosmos

    Render-ready assets for scene diversity

Physics

  • Luna

    Rigid-body simulation: collisions, falling and stacked objects, moving machinery

  • Chaos Phoenix

    Fire, smoke and fluids

  • Chaos Anima

    Crowds, pedestrians and vehicles

Scale

  • Chaos Vantage

    Real-time ray tracing for high-volume generation

  • Chaos Cloud

    Distributed cloud rendering, in production today

  • Chaos

    10,000 or 10 million images, at lower cost in one ecosystem

How we start

Low commitment. High return.

Scoping & setup

01Free
  • Define the target AI task: detection, segmentation, depth or other
  • Identify the input we build your digital twin from
  • Agree on dataset format and quality benchmarks

Scale & iterate

03Priced on results
  • Full training-scale datasets based on pilot results
  • Ongoing variation to keep improving performance
  • Priority access to new capabilities and input on the roadmap
Next step

Tell us what your model needs to see. We'll build it for you.

Meet the team building Luna.

Allan PooreCPO
Mihail SergeevPrincipal Engineer
Felix RuhEnterprise Relationships

Talk to the Luna team

Fill out the form and the team will get back to you.

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Common questions

Questions teams ask before the first call.

How much input do you need to build a twin?

The more, the better. It really depends on the use case and on how much you can share.

Does the twin have to look identical to the real thing?

The level of detail depends on your task. We focus on geometry, materials, light, camera and motion, then measure how the data performs when you train your model.

In case you have any specific requirements about parts of the digital twin that should be especially accurate, let us know in advance.

What do you deliver, and in which formats?

Images, panoramas, video or Gaussian splats, each with its label passes, in the format your pipeline uses. We can also deliver motion and sensor data in tabular format.

We can deliver custom data formats on request.

Who owns the digital twin and the rendered datasets?

Once the project is complete and the dataset is produced, you own it completely.

How long does a pilot take?

Depending on availability, from a few days to a few weeks.

Can it plug into our training pipeline?

Luna is currently not generally available via an API. Tell us more about your use case and we can find a way to make it work.

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