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Top-Down System for Multi-Person 3D Absolute Pose Estimation from Monocular Videos.

Amal El Kaid1,2,3, Denis Brazey3, Vincent Barra1

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This study introduces Root-GAST-Net, a real-time system for estimating multiple people's 3D absolute poses from a single camera. It achieves state-of-the-art performance, overcoming previous resource limitations.

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3D multi-person pose estimationabsolute posesartificial intelligencecamera-centric coordinatescomputer visiondeep-learning

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Recent advancements in 2D and 3D pose estimation exist.
  • Real-world applications need 3D absolute poses and inter-person distances.
  • Single-view 3D absolute pose estimation remains challenging and resource-intensive.

Purpose of the Study:

  • To propose a real-time framework for multi-person 3D absolute pose estimation from a monocular camera.
  • To overcome the limitations of existing resource-heavy systems.
  • To enable accurate depth and distance estimations between individuals in a scene.

Main Methods:

  • A novel top-down framework integrating human detection, 2D pose estimation, 3D root-relative pose reconstruction, and root depth estimation.
  • Utilizes modified GAST-Net and RootNet architectures, forming the Root-GAST-Net system.
  • Evaluated quantitatively and qualitatively on Human3.6M and MuPoTS-3D benchmark datasets.

Main Results:

  • Root-GAST-Net achieves real-time performance at 15 fps on an Nvidia GeForce GTX 1080.
  • Significantly outperforms current state-of-the-art methods on the MuPoTS-3D dataset across all metrics.
  • Demonstrates efficiency and effectiveness through comprehensive evaluations.

Conclusions:

  • The proposed Root-GAST-Net framework successfully addresses the challenge of multi-person 3D absolute pose estimation from monocular cameras.
  • It offers a resource-efficient and high-performance solution for real-world applications requiring accurate 3D human pose information.
  • The system provides a significant improvement over existing methods, paving the way for more advanced computer vision applications.