# Markerless 3D human pose tracking through multiple cameras and AI: Enabling high accuracy, robustness, and real-time performance

arXiv: https://arxiv.org/abs/2303.18119 · HTML: https://mlsanity.com/paper/2303.18119/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time](https://mlsanity.com/paper/2211.03375/) (2022)
- [Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods](https://mlsanity.com/paper/2006.01423/) (2020)
- [A review of 3D human pose estimation algorithms for markerless motion capture](https://mlsanity.com/paper/2010.06449/) (2020)
- [MetaFi: Device-Free Pose Estimation via Commodity WiFi for Metaverse Avatar Simulation](https://mlsanity.com/paper/2208.10414/) (2022)
- [Consensus-based Optimization for 3D Human Pose Estimation in Camera Coordinates](https://mlsanity.com/paper/1911.09245/) (2019)
- [Simultaneous Multi-View Camera Pose Estimation and Object Tracking with Square Planar Markers](https://mlsanity.com/paper/2103.09141/) (2021)
- [Open-VICO: An Open-Source Gazebo Toolkit for Vision-based Skeleton Tracking in Human-Robot Collaboration](https://mlsanity.com/paper/2203.14733/) (2022)

## Built on by (most cited first, 2 of 2)

- [HFGaussian: Learning Generalizable Gaussian Human with Integrated Human Features](https://mlsanity.com/paper/2411.03086/) (2024)
- [Facial Movement Dynamics Reveal Workload During Complex Multitasking](https://mlsanity.com/paper/2603.17767/) (2026)

## Abstract (arXiv)

Tracking 3D human motion in real-time is crucial for numerous applications across many fields. Traditional approaches involve attaching artificial fiducial objects or sensors to the body, limiting their usability and comfort-of-use and consequently narrowing their application fields. Recent advances in Artificial Intelligence (AI) have allowed for markerless solutions. However, most of these methods operate in 2D, while those providing 3D solutions compromise accuracy and real-time performance. To address this challenge and unlock the potential of visual pose estimation methods in real-world scenarios, we propose a markerless framework that combines multi-camera views and 2D AI-based pose estimation methods to track 3D human motion. Our approach integrates a Weighted Least Square (WLS) algorithm that computes 3D human motion from multiple 2D pose estimations provided by an AI-driven method. The method is integrated within the Open-VICO framework allowing simulation and real-world execution. Several experiments have been conducted, which have shown high accuracy and real-time performance, demonstrating the high level of readiness for real-world applications and the potential to revolutionize human motion capture.