3D human pose estimation with siamese equivariant embedding

Márton Véges, Viktor Varga, András Lőrincz

Research output: Contribution to journalArticle

5 Citations (Scopus)


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
Publication statusPublished - Apr 28 2019


  • 3D pose estimation
  • Equivariant embedding
  • Siamese network

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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