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3D Hand Joints Position Estimation with Graph Convolutional Networks: A GraphHands Baseline

  • John-Alejandro Castro-VargasEmail author
  • Alberto Garcia-Garcia
  • Sergiu Oprea
  • Pablo Martinez-Gonzalez
  • Jose Garcia-Rodriguez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1093)

Abstract

State-of-the-art deep learning-based models used to address hand challenges, e.g. 3D hand joint estimation, need a vast amount of annotated data to achieve a good performance. The lack of data is a problem of paramount importance. Consequently, the use of synthetic datasets for training deep learning models is a trend and represents a promising avenue to improve existing approaches. Nevertheless, currently existing synthetic datasets lack of accurate and complete annotations, realism, and also rich hand-object interactions. For this purpose, in our work we present a synthetic dataset featuring rich hand-object interactions in photorealistic scenarios. The applications of our dataset for hand-related challenges are unlimited. To validate our data, we propose an initial approach to 3D hand joint estimation using a graph convolutional network feeded with point cloud data. Another point in favour of our dataset is that interactions are performed using realistic objects extracted from the YCB dataset. This could allow to test trained systems with our synthetic dataset using images/videos manipulating the same objects in real life.

Keywords

Synthetic dataset Photorealism Hand-object interaction 3D hand joint estimation 

Notes

Acknowledgements

This work has been funded by the Spanish Government grant TIN2016-76515-R for the COMBAHO project, supported with Feder funds. This work has also been supported by three Spanish national grants for PhD studies (FPU15/04516, FPU17/00166, and ACIF/2018/197), by the University of Alicante project GRE16-19, and by the Valencian Government project GV/2018/022. Experiments were made possible by a generous hardware donation from NVIDIA.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • John-Alejandro Castro-Vargas
    • 1
    Email author
  • Alberto Garcia-Garcia
    • 1
  • Sergiu Oprea
    • 1
  • Pablo Martinez-Gonzalez
    • 1
  • Jose Garcia-Rodriguez
    • 1
  1. 1.3D Perception LabUniversity of AlicanteAlicanteSpain

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