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Skeleton Extraction of Dance Sequences from 3D Points Using Convolutional Neural Networks Based on a New Developed C3D Visualization Interface

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The Challenges of the Digital Transformation in Education (ICL 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 917))

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Abstract

A combined approach, involving 3D spatial datasets, noise removal prepossessing and deep learning regression approaches for the estimation of rough skeleton data, is presented in this paper. The application scenario involved data sequences from Greek traditional dances. In particular, a visualization application interface was developed allowing the user to load the C3D sequences, edit the data and remove possible noise. The interface was developed using the OpenGL language and is able to parse aby C3D format file. The interface is supported by several functionalities such as a pre-processing of the 3D point data and noise removal of 3D points that fall apart from the human skeleton. The main research innovation of this paper is the use of a deep machine learning framework through which human skeleton can be extracted. The points are selected on the use of a Convolutional Neural Network (CNN) model. Experimental results on real-life dances being captured by the Vicon motion capturing system are presented to show the great performance of the proposed scheme.

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Notes

  1. 1.

    The developed user interface can be found in https://github.com/JohnCrabs/Crabs3Dv122.

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Acknowledgments

This work was supported by the EU H2020 TERPSICHORE project “Transforming Intangible Folkloric Performing Arts into Tangible Choreographic Digital Objects” under the grant agreement 691218.

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Authors and Affiliations

  1. National Technical University of Athens, 15773, Zografou, Athens, Greece

    Ioannis Kavouras, Eftychios Protopapadakis, Anastasios Doulamis & Nikolaos Doulamis

Authors
  1. Ioannis Kavouras
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  2. Eftychios Protopapadakis
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  3. Anastasios Doulamis
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  4. Nikolaos Doulamis
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Corresponding author

Correspondence to Anastasios Doulamis .

Editor information

Editors and Affiliations

  1. Carinthia University of Applied Sciences , Villach, Kärnten, Austria

    Michael E. Auer

  2. Department of Informatics, Aristotle University of Thessaloniki , Thessaloniki, Greece

    Thrasyvoulos Tsiatsos

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Kavouras, I., Protopapadakis, E., Doulamis, A., Doulamis, N. (2019). Skeleton Extraction of Dance Sequences from 3D Points Using Convolutional Neural Networks Based on a New Developed C3D Visualization Interface. In: Auer, M., Tsiatsos, T. (eds) The Challenges of the Digital Transformation in Education. ICL 2018. Advances in Intelligent Systems and Computing, vol 917. Springer, Cham. https://doi.org/10.1007/978-3-030-11935-5_26

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