Auto-generating virtual human behavior by understanding user contexts

Hanseob Kim, Ghazanfar Ali, Seungwon Kim, Gerard J. Kim, Jae In Hwang

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

Abstract

Virtual humans are most natural and effective when it can act out and animate verbal/gestural actions. One popular method to realize this is to infer the actions from predefined phrases. This research aims to provide a more flexible method to activate various behaviors straight from natural conversations. Our approach uses BERT as the backbone for natural language understanding and, on top of it, a jointly learned sentence classifier (SC) and entity classifier (EC). The SC classifies the input into conversation or action, and EC extracts the entities for the action. The pilot study has shown promising results with high perceived naturalness and positive experiences.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages591-592
Number of pages2
ISBN (Electronic)9780738113678
DOIs
Publication statusPublished - 2021 Mar
Event2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021 - Virtual, Lisbon, Portugal
Duration: 2021 Mar 272021 Apr 3

Publication series

NameProceedings - 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021

Conference

Conference2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021
Country/TerritoryPortugal
CityVirtual, Lisbon
Period21/3/2721/4/3

Keywords

  • Computing methodologies
  • Human-centered computing
  • Interaction design process and methods
  • Natural language processing
  • Virtual reality

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Media Technology
  • Modelling and Simulation

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