Towards Viewpoint Invariant 3D Human Pose Estimation

  • Albert Haque
  • Boya Peng
  • Zelun Luo
  • Alexandre Alahi
  • Serena Yeung
  • Li Fei-Fei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9905)

Abstract

We propose a viewpoint invariant model for 3D human pose estimation from a single depth image. To achieve this, our discriminative model embeds local regions into a learned viewpoint invariant feature space. Formulated as a multi-task learning problem, our model is able to selectively predict partial poses in the presence of noise and occlusion. Our approach leverages a convolutional and recurrent network architecture with a top-down error feedback mechanism to self-correct previous pose estimates in an end-to-end manner. We evaluate our model on a previously published depth dataset and a newly collected human pose dataset containing 100 K annotated depth images from extreme viewpoints. Experiments show that our model achieves competitive performance on frontal views while achieving state-of-the-art performance on alternate viewpoints.

Supplementary material

419956_1_En_10_MOESM1_ESM.pdf (5 mb)
Supplementary material 1 (pdf 5122 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Albert Haque
    • 1
  • Boya Peng
    • 1
  • Zelun Luo
    • 1
  • Alexandre Alahi
    • 1
  • Serena Yeung
    • 1
  • Li Fei-Fei
    • 1
  1. 1.Stanford UniversityStanfordUSA

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