MediaPipe Hands: On-device Real-time Hand Tracking
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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.
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Authors
7Topics & keywords
Topics
Keywords
- Pipeline (software)
- Computer science
- Tracking (education)
- Computer vision
- RGB color model
- Landmark
- Inference
- Real-time computing
UN Sustainable Development Goals
- Industry, innovation and infrastructure
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