3D human pose estimation with siamese equivariant embedding

Márton Véges, Viktor Varga, A. Lőrincz

Research output: Article

Abstract

In monocular 3D human pose estimation a common setup is to first detect 2D positions and then lift the detection into 3D coordinates. Many algorithms suffer from overfitting to camera positions in the training set. We propose a siamese architecture that learns a rotation equivariant hidden representation to reduce the need for data augmentation. Our method is evaluated on multiple databases with different base networks and shows a consistent improvement of error metrics. It achieves state-of-the-art cross-camera error rate among algorithms that use estimated 2D joint coordinates only.

Original languageEnglish
Pages (from-to)194-201
Number of pages8
JournalNeurocomputing
Volume339
DOIs
Publication statusPublished - ápr. 28 2019

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Cameras
Joints
Databases

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Cite this

3D human pose estimation with siamese equivariant embedding. / Véges, Márton; Varga, Viktor; Lőrincz, A.

In: Neurocomputing, Vol. 339, 28.04.2019, p. 194-201.

Research output: Article

Véges, Márton ; Varga, Viktor ; Lőrincz, A. / 3D human pose estimation with siamese equivariant embedding. In: Neurocomputing. 2019 ; Vol. 339. pp. 194-201.
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