emg2pose
Hand tracking from the wrist — no camera required
Latest: NeurIPS 2024 release (arXiv Dec 2024)
A benchmark for reconstructing full hand pose from wrist sEMG alone: 370 hours of 2kHz, 16-channel EMG from 193 users, paired with ground-truth hand pose from a 26-camera motion-capture rig across 29 gesture stages. Released by Reality Labs at NeurIPS 2024 alongside emg2qwerty — scale comparable to vision-based hand-pose datasets.
Why it matters
A benchmark for recovering full hand pose from wrist EMG alone: 370 hours of 2kHz, 16-channel recordings from 193 users, paired with ground truth from a 26-camera motion-capture rig across 29 gesture stages. Its scale rivals vision-based hand-pose datasets and makes camera-free hand tracking a tractable public research problem.
Facts
- 370 hours from 193 users captured in a 26-camera mocap dome — hand tracking with no camera at all, the exact capability that lets the Neural Band track fingers 'without the need to be in view of a camera.'
Try it yourself
Dataset and baselines on GitHub ↗ Read the paper ↗
Lineage
Sources
arXiv ↗GitHub · emg2pose ↗Meta AI blog ↗