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PERFORM: Perceptual Approach for Adding OCEAN Personality to Human Motion Using Laban Movement Analysis
Journal article   Peer reviewed

PERFORM: Perceptual Approach for Adding OCEAN Personality to Human Motion Using Laban Movement Analysis

Funda Durupinar, Mubbasir Kapadia, Susan Deutsch, Michael Neff and Norman I. Badler
ACM transactions on graphics, v 36(1)
01 Feb 2017

Abstract

Computer Science Computer Science, Software Engineering Science & Technology Technology
A major goal of research on virtual humans is the animation of expressive characters that display distinct psychological attributes. Body motion is an effective way of portraying different personalities and differentiating characters. The purpose and contribution of this work is to describe a formal, broadly applicable, procedural, and empirically grounded association between personality and body motion and apply this association to modify a given virtual human body animation that can be represented by these formal concepts. Because the body movement of virtual characters may involve different choices of parameter sets depending on the context, situation, or application, formulating a link from personality to body motion requires an intermediate step to assist generalization. For this intermediate step, we refer to Laban Movement Analysis, which is a movement analysis technique for systematically describing and evaluating human motion. We have developed an expressive human motion generation system with the help of movement experts and conducted a user study to explore how the psychologically validated OCEAN personality factors were perceived in motions with various Laban parameters. We have then applied our findings to procedurally animate expressive characters with personality, and validated the generalizability of our approach across different models and animations via another perception study.

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69 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Software Engineering
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