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Motion Capture Synthesis with Adversarial Learning

  • Qi WangEmail author
  • Thierry Artières
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10498)

Abstract

We propose a new statistical modeling approach that we call Sequential Adversarial Auto-encoder (SAAE) for learning a synthesis model for motion sequences. This model exploits the adversarial idea that has been popularized in the machine learning field for learning accurate generative models. We further propose a conditional variant of this model that takes as input an additional information such as the activity which is performed in a sequence, or the emotion with which it is performed, and which allows to perform synthesis in context.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Ecole Centrale MarseilleMarseilleFrance
  2. 2.LIF, Université d’Aix Marseille and CNRSMarseilleFrance

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