MoViz: A Visualization Tool for Comparing Motion Capture Data Clustering Algorithms

Lucas Liu, Duri Long, Brian Magerko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Motion capture data is useful for machine learning applications in a variety of domains (e.g. movement improvisation, physical therapy, character animation in games), but many of these domains require large, diverse datasets with data that is difficult to label. This has precipitated the use of unsupervised learning algorithms for analyzing motion capture datasets. However, there is a distinct lack of tools that aid in the qualitative evaluation of these unsupervised algorithms. In this paper, we present the design of MoViz, a novel visualization tool that enables comparative qualitative evaluation of otherwise "black-box" algorithms for pre-processing and clustering large and diverse motion capture datasets. We applied MoViz to the evaluation of three different gesture clustering pipelines used in the LuminAI improvisational dance system. This evaluation revealed features of the pipelines that may not otherwise have been apparent, suggesting directions for iterative design improvements. This use case demonstrates the potential for this tool to be used by researchers and designers in the field of movement and computing seeking to better understand and evaluate the algorithms they are using to make sense of otherwise intractably large and complex datasets.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th International Conference on Movement and Computing, MOCO 2020
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450375054
DOIs
StatePublished - Jul 15 2020
Event7th International Conference on Movement and Computing, MOCO 2020 - Jersey City, Virtual, United States
Duration: Jul 15 2020Jul 17 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference7th International Conference on Movement and Computing, MOCO 2020
Country/TerritoryUnited States
CityJersey City, Virtual
Period7/15/207/17/20

Keywords

  • explainable AI
  • gesture clustering
  • motion capture data
  • movement improvisation
  • unsupervised learning
  • visualization

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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