First-person capture of real human work
123,000+ verified hours
339,000+ clips · 2.42M+ annotations
aggregated & de-identified · capture-quality snapshot · 2026-06
The library

Indexed by task, not by folder.

Every clip sits in an ontology — occupation × scene × procedure. Pick an industry and pull exactly the slice you need, down to the bench it was shot at.

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CNet ontology · 2,660 occupations · 299 scenes · 19,147 processes
Open the data viewer →
The annotation

One clip, taken apart.

Every object the hands touch, tracked and named. Every motion placed in 3D. Every segment written out — and the reason it followed.

objects · tracked · namedDragon fruit, foam-net bag, picking tongs — boxed per frame, not per clip.
3D pose · object topologyBody, head and hands in 3D — plus how the objects relate to each other.
action · environment · execution · inferenceFour causality layers, so a model learns why a motion followed.
plain-language segmentsWritten out on a shared timeline. Delivered LeRobot v3 / RLDS.
Capture

Hands and body, recovered in 3D.

The hand mesh placed back into a real point cloud, and full-body pose across two scenes — all recovered off ordinary first-person capture.

Hand tracking · 2D keypoints + mesh in point cloud · 1:11
Full-body pose · outdoor · input · mesh · skeleton · 0:33
Full-body pose · workshop · input · mesh · skeleton · 1:33
Egocentric capture · synchronized view · 0:30

Tell us what you're training.

What tasks, what environments, what keeps failing. We'll tell you whether we already have it.

or [email protected]
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The physical world was never written down.
We are writing it.

The origin point of machine reality
We're hiring Shenzhen · Singapore · remote considered · [email protected]

Building the capture layer nobody else owns. These are open.

01Data engineeringPipeline · storage · delivery
02Capture standardsProtocol · fleet ops · field
03Quality systemsAuto QC · human review
…3D perception, and moreWrite to the address above