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Learning to Gesticulate by Observation Using a Deep Generative Approach

  • Unai Zabala
  • Igor RodriguezEmail author
  • José María Martínez-Otzeta
  • Elena Lazkano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11876)

Abstract

The goal of the system presented in this paper is to develop a natural talking gesture generation behavior for a humanoid robot, by feeding a Generative Adversarial Network (GAN) with human talking gestures recorded by a Kinect. A direct kinematic approach is used to translate from human poses to robot joint positions. The provided videos show that the robot is able to use a wide variety of gestures, offering a non-dreary, natural expression level.

Keywords

Social robots Motion capturing and imitation Generative Adversarial Networks Talking movements 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Science and Artificial Intelligence, Faculty of InformaticsUPV/EHUDonostiaSpain

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