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Skeleton Transformer Networks: 3D Human Pose and Skinned Mesh from Single RGB Image

  • Yusuke YoshiyasuEmail author
  • Ryusuke Sagawa
  • Ko Ayusawa
  • Akihiko Murai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11364)

Abstract

In this paper, we present Skeleton Transformer Networks (SkeletonNet), an end-to-end framework that can predict not only 3D joint positions but also 3D angular pose (bone rotations) of a human skeleton from a single color image. This in turn allows us to generate skinned mesh animations. Here, we propose a two-step regression approach. The first step regresses bone rotations in order to obtain an initial solution by considering skeleton structure. The second step performs refinement based on heatmap regressor using a 3D pose representation called cross heatmap which stacks heatmaps of xy and zy coordinates. By training the network using the proposed 3D human pose dataset that is comprised of images annotated with 3D skeletal angular poses, we showed that SkeletonNet can predict a full 3D human pose (joint positions and bone rotations) from a single image in-the-wild.

Keywords

Convolutional neural networks 3D human pose Skeleton 

Notes

Acknowledgment

I would like to thank Rie Nishihama and CNRS-AIST JRL members for supporting us constructing 3D human pose dataset. This work was partly supported by JSPS Kakenhi No. 17K18420 and No. 18H03315.

Supplementary material

Supplementary material 1 (mp4 8666 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yusuke Yoshiyasu
    • 1
    • 2
    Email author
  • Ryusuke Sagawa
    • 1
    • 2
  • Ko Ayusawa
    • 1
    • 2
  • Akihiko Murai
    • 1
  1. 1.National Institute of Advanced Industrial Science and Technology (AIST)TokyoJapan
  2. 2.CNRS-AIST JRL (Joint Robotics Laboratory), UMI3218/RLTsukubaJapan

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