{"title":"Understanding the Impact of Visual and Kinematic Information on the Perception of Physicality Errors","authors":"Goksu Yamac, Carol O’Sullivan, Michael Neff","doi":"10.1145/3660636","DOIUrl":null,"url":null,"abstract":"<p>Errors that arise due to a mismatch in the dynamics of a person’s motion and the visualized movements of their avatar in virtual reality are termed ‘physicality errors’ to distinguish them from simple physical errors, such as footskate. Physicality errors involve plausible motions, but with dynamic inconsistencies. Even with perfect tracking and ideal virtual worlds, such errors are inevitable in virtual reality whenever a person adopts an avatar that does not match their own proportions or lifts a virtual object that appears heavier than the movement of their hand. This study investigates people’s sensitivity to physicality errors in order to understand when they are likely to be noticeable and need to be mitigated. It uses a simple, well-understood exercise of a dumbbell lift to explore the impact of motion kinematics and varied sources of visual information, including changing body size, changing the size of manipulated objects, and displaying muscular strain. Results suggest that kinematic (motion) information has a dominant impact on perception of effort, but visual information, particularly the visual size of the lifted object, has a strong impact on perceived weight. This can lead to perceptual mismatches which reduce perceived naturalness. Small errors may not be noticeable, but large errors reduce naturalness. Further results are discussed, which inform the requirements for animation algorithms.</p>","PeriodicalId":50921,"journal":{"name":"ACM Transactions on Applied Perception","volume":"219 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Applied Perception","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3660636","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Errors that arise due to a mismatch in the dynamics of a person’s motion and the visualized movements of their avatar in virtual reality are termed ‘physicality errors’ to distinguish them from simple physical errors, such as footskate. Physicality errors involve plausible motions, but with dynamic inconsistencies. Even with perfect tracking and ideal virtual worlds, such errors are inevitable in virtual reality whenever a person adopts an avatar that does not match their own proportions or lifts a virtual object that appears heavier than the movement of their hand. This study investigates people’s sensitivity to physicality errors in order to understand when they are likely to be noticeable and need to be mitigated. It uses a simple, well-understood exercise of a dumbbell lift to explore the impact of motion kinematics and varied sources of visual information, including changing body size, changing the size of manipulated objects, and displaying muscular strain. Results suggest that kinematic (motion) information has a dominant impact on perception of effort, but visual information, particularly the visual size of the lifted object, has a strong impact on perceived weight. This can lead to perceptual mismatches which reduce perceived naturalness. Small errors may not be noticeable, but large errors reduce naturalness. Further results are discussed, which inform the requirements for animation algorithms.
期刊介绍:
ACM Transactions on Applied Perception (TAP) aims to strengthen the synergy between computer science and psychology/perception by publishing top quality papers that help to unify research in these fields.
The journal publishes inter-disciplinary research of significant and lasting value in any topic area that spans both Computer Science and Perceptual Psychology. All papers must incorporate both perceptual and computer science components.