4.5 Article

Avoiding virtual humans in a constrained environment: Exploration of novel behavioural measures

期刊

COMPUTERS & GRAPHICS-UK
卷 110, 期 -, 页码 162-172

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cag.2023.01.001

关键词

Perception; VR; Personality; Virtual characters; Trajectory analysis

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In computer animation, the creation of believable and engaging virtual characters has been a long-standing goal. This study focuses on the qualities of human motion and investigates the perception of motion based on distinct movement patterns observed in individuals with neurotic and emotionally stable personality traits. The researchers conducted an experiment in virtual reality to study participants' avoidance behavior when encountering these virtual characters in a constrained environment. The study proposes the use of behavioral measures, such as a choice task and a "turning point" metric, to investigate character motion in future research.
In computer animation, the creation of believable and engaging virtual characters has been a long-lasting endeavour. While researchers investigated several aspects of character design, not many studies focused on the qualities of biological human motion itself. We approached the perception of motion from the perspective of distinct movement patterns which can be observed on people with neurotic and emotionally stable personality traits. We designed an experiment in virtual reality, using a photo -realistic metro scenario, where we studied the avoidance behaviour of participants when encountering these two types of virtual characters in a constrained environment. We also make a contribution by successfully implementing two behavioural measures in particular: a choice task, and a novel 'turning point' metric, which calculates the point in the trajectory when people turned to avoid the character. Our results indicate that users' behaviour is affected by character's motion and we propose the use of these behavioural measures to investigate other aspects of character motion in future research.(c) 2023 Elsevier Ltd. All rights reserved.

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