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Perception of Human Motion Similarity Based on Laban Movement Analysis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Metrics of similarity between human motion sequences are central to many motion retrieval, classification, and synthesis techniques. However, research on distance measures shows a lack of correlation between human perception and commonly used metrics such as dynamic time warping distance. This paper analyzes the perception of human motion similarity based on the intrinsic qualities of motion described by the Effort component of Laban Movement Analysis. We conducted an Amazon Mechanical Turk study to collect human judgments on the perceptual similarity of various motion styles to the baseline conditions. The results indicate that Effort factors impacting the flow of movement are perceptually more distinguishable than factors affecting the timing, weight, and spatial attention. The results also confirm the lack of correlation between the human perception of motion similarity and standard measures.

Original languageEnglish
Title of host publicationProceedings - SAP 2021
Subtitle of host publicationACM Symposium on Applied Perception
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450386630
DOIs
StatePublished - Sep 16 2021
Event2021 ACM Symposium on Applied Perception, SAP 2021 - Virtual, Online, France
Duration: Sep 16 2021 → Sep 17 2021

Publication series

NameProceedings - SAP 2021: ACM Symposium on Applied Perception

Conference

Conference2021 ACM Symposium on Applied Perception, SAP 2021
Country/TerritoryFrance
CityVirtual, Online
Period9/16/21 → 9/17/21

ASJC Scopus Subject Areas

  • Computational Theory and Mathematics
  • Software
  • Applied Mathematics
  • Theoretical Computer Science

Keywords

  • character animation
  • human motion
  • Laban Movement Analysis
  • perceptual motion similarity

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