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3D Research

, 8:25 | Cite as

Learning Motion Features for Example-Based Finger Motion Estimation for Virtual Characters

  • Christos MousasEmail author
  • Christos-Nikolaos Anagnostopoulos
3DR Express

Abstract

This paper presents a methodology for estimating the motion of a character’s fingers based on the use of motion features provided by a virtual character’s hand. In the presented methodology, firstly, the motion data is segmented into discrete phases. Then, a number of motion features are computed for each motion segment of a character’s hand. The motion features are pre-processed using restricted Boltzmann machines, and by using the different variations of semantically similar finger gestures in a support vector machine learning mechanism, the optimal weights for each feature assigned to a metric are computed. The advantages of the presented methodology in comparison to previous solutions are the following: First, we automate the computation of optimal weights that are assigned to each motion feature counted in our metric. Second, the presented methodology achieves an increase (about 17%) in correctly estimated finger gestures in comparison to a previous method.

Keywords

Finger motion Motion estimation Character animation Motion features Features pre-processing Metric learning 

Supplementary material

Supplementary material 1 (mp4 18439 KB)

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Copyright information

© 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Christos Mousas
    • 1
    Email author
  • Christos-Nikolaos Anagnostopoulos
    • 2
  1. 1.Graphics and Entertainment Technology Lab, Department of Computer ScienceSouthern Illinois UniversityCarbondaleUSA
  2. 2.Department of Cultural Technology and CommunicationUniversity of the AegeanMytileneGreece

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