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BlazePose GHUM Holistic: Real-time 3D Human Landmarks and Pose Estimation

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arxiv 2206.11678 v1 pith:3BSX5AYJ submitted 2022-06-23 cs.CV

Ivan Grishchenko , Valentin Bazarevsky , Andrei Zanfir , Eduard Gabriel Bazavan , Mihai Zanfir , Richard Yee , Karthik Raveendran , Matsvei Zhdanovich
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Matthias Grundmann Cristian Sminchisescu
This is my paper · ORCID
classification cs.CV
keywords blazeposebodyestimationghumholisticlandmarksposehuman
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We present BlazePose GHUM Holistic, a lightweight neural network pipeline for 3D human body landmarks and pose estimation, specifically tailored to real-time on-device inference. BlazePose GHUM Holistic enables motion capture from a single RGB image including avatar control, fitness tracking and AR/VR effects. Our main contributions include i) a novel method for 3D ground truth data acquisition, ii) updated 3D body tracking with additional hand landmarks and iii) full body pose estimation from a monocular image.

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data

    cs.CV 2026-07 reject novelty 5.0 of 10

    A STGCN-GRU-BiLSTM model detects the frame of ground impact in falls from 3D skeleton data, reaching a reported 97.5% accuracy on a relabeled UP-Fall subset.

  2. Metamorphic Testing for Pose Estimation Systems

    cs.SE 2025-02 conditional novelty 5.0 of 10

    MET-POSE uses metamorphic rules to test pose-estimation systems without ground-truth labels, and on Mediapipe Holistic it detects faults at similar or higher rates than classic labeled testing.

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