The model is the easy part: ask Claude or ChatGPT and you have one in a minute. What it
cannot invent is thousands of real signs, from different Deaf bodies, labelled frame by
frame. That is Épée. Below, the same model trained twice, once on each release.
CAMERA OFF
STEP 1 · SETUP
Which hand do you sign with?
It only sets which hand goes in which slot. Nothing is recorded.
STEP 1 · SETUP · NO SIGNING
Calibration
Four short poses, about 11 seconds. Nothing is recorded.
STEP 2 · YOUR TURN
Just sign
It starts on its own when you move, and reads when you stop.
Reading your sign
READY
No camera needed: the replay runs real clips from a signer in neither training set.
WHAT IT READS ON A STRANGER
8 signs it reads at 70%+
overall, unseen signer59% → 69%
signs with 5+ examples76 → 205
signs with 10+ examples19 → 71
READ IT RIGHT. Listed: only signs that clear 70% recall on a signer the model
never saw, with the precision to match. 24 signs is the frozen benchmark, not the
corpus ceiling: depth makes a wider vocabulary trainable, it does not mean it is
trained. One sign, one word is the first step of translation, not translation:
sentences need grammar on top, which is what the parallel grid trains.
TRAINED ON ÉPÉE v0.2
·
replay a clip to compare
TRAINED ON ÉPÉE v0.3
·
replay a clip to compare