English

MediaPipe Hands: On-device Real-time Hand Tracking

Computer Vision and Pattern Recognition 2020-06-19 v1

Abstract

We present a real-time on-device hand tracking pipeline that predicts hand skeleton from single RGB camera for AR/VR applications. The pipeline consists of two models: 1) a palm detector, 2) a hand landmark model. It's implemented via MediaPipe, a framework for building cross-platform ML solutions. The proposed model and pipeline architecture demonstrates real-time inference speed on mobile GPUs and high prediction quality. MediaPipe Hands is open sourced at https://mediapipe.dev.

Keywords

Cite

@article{arxiv.2006.10214,
  title  = {MediaPipe Hands: On-device Real-time Hand Tracking},
  author = {Fan Zhang and Valentin Bazarevsky and Andrey Vakunov and Andrei Tkachenka and George Sung and Chuo-Ling Chang and Matthias Grundmann},
  journal= {arXiv preprint arXiv:2006.10214},
  year   = {2020}
}

Comments

5 pages, 7 figures; CVPR Workshop on Computer Vision for Augmented and Virtual Reality, Seattle, WA, USA, 2020

R2 v1 2026-06-23T16:25:10.158Z