A handle bar metaphor for virtual object manipulation with mid-air interaction
Abstract
Commercial 3D scene acquisition systems such as the Microsoft Kinect sensor can reduce the cost barrier of realizing mid-air interaction. However, since it can only sense hand position but not hand orientation robustly, current mid-air interaction methods for 3D virtual object manipulation often require contextual and mode switching to perform translation, rotation, and scaling, thus preventing natural continuous gestural interactions. A novel handle bar metaphor is proposed as an effective visual control metaphor between the user's hand gestures and the corresponding virtual object manipulation operations. It mimics a familiar situation of handling objects that are skewered with a bimanual handle bar. The use of relative 3D motion of the two hands to design the mid-air interaction allows us to provide precise controllability despite the Kinect sensor's low image resolution. A comprehensive repertoire of 3D manipulation operations is proposed to manipulate single objects, perform fast constrained rotation, and pack/align multiple objects along a line. Three user studies were devised to demonstrate the efficacy and intuitiveness of the proposed interaction techniques on different virtual manipulation scenarios.
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The indirect manipulation techniques are either more precise or fast to use.
“These indirect manipulation techniques are either more precise [36,37] or fast to use [32,37].”
From Trade-Off between Task Accuracy, Task Completion Time and Naturalness for Direct Object Manipulation in Virtual Reality (2022) · cites this paper as [32]
The indirect manipulation techniques suffer from either hand tiredness or increased learning curve.
“However, these techniques suffer from either hand tiredness [32] or increased learning curve [35,36] which are important for medical context.”
From Trade-Off between Task Accuracy, Task Completion Time and Naturalness for Direct Object Manipulation in Virtual Reality (2022) · cites this paper as [32]
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- Authors
- Song, P., Goh, W. B., Hutama, W., Fu, C.-W., & Liu, X.
- Affiliations
- Nanyang Technological University (SG)
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2012-05-05
- Pages
- 1297-1306
- OpenAlex
- W2102284405
- Cited by
- 210
- References
- 30
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 11 · 2023: 17 · 2022: 18 · 2021: 17 · 2020: 14 · 2019: 17 · 2018: 16 · 2017: 16 · 2016: 16 · 2015: 18 · 2014: 20 · 2013: 12 · 2012: 3
- FWCI
- 11.92
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Interactive and Immersive Displays
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Keywords
- mid-air interaction, virtual object manipulation, hand gestures, Kinect sensor, bimanual interaction, object alignment, gesture-based interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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