A deep reservoir of egocentric human activity built for serious model development, not demo-day theatre.
THE DATA LAYER FOR PHYSICAL AIGive embodied AI
Give embodied AI
a first-person view.
1,000,000 hours of egocentric human activity, captured and annotated for the machines that have to act in the real world.
Access the data layer ↗LIVE SAMPLE / 01FUSION / ANNOTATED
01 / Z7NOOUglovkREPRESENTATIVE CLIP
1,000,000 hours / egocentricHuman motion × machine action
A DIFFERENT KIND OF TRAINING SIGNAL
The missing layer
The missing layer
between seeing and doing.
Vision tells a model what is in front of it. Hand data teaches it how the world changes when it acts.
Our one-million-hour egocentric corpus turns human motion into a high-fidelity training layer for perception, planning, and control.
CORPUS / 1M HOURSANNOTATED SIGNAL
HAND DATAMOTION / INTENT / CONTROLREADY TO TRAIN
AT A GLANCEONE MILLION HOURS / MANY BREAKTHROUGHS
Motion-rich fusion sequences preserve the continuity between perception, movement, and intent.
Labels turn raw motion into the context a model needs to learn what an action means.
EXPLORE THE SAMPLESixteen more windows
Sixteen more windows
into the corpus.
The hero clip is the first look. Browse the remaining representative sequences below to see the range of motion, capture, and annotation in the wider dataset.
WHY IT MATTERS
From human motion
From human motion
to machine agency.
01
Teach action recognition
Move beyond “what is there” to “what is happening” — with motion, context, and intent in the loop.
02
Train robot policies
Close the gap between a plan and a physical action with a richer, more grounded training signal.
03
Stress-test embodied models
Build evaluation around the moments where perception, planning, and control finally meet.
BUILD ON THE ADVANTAGEPut a million hours
Put a million hours
to work.
Tell us what your model needs to understand. We will help you find the right slice of the corpus — and the fastest path from signal to capability.
hello@tokenyes.ai ↗