Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
10-2024
Abstract
Mixed Reality enables hybrid workspaces where physical and virtual monitors are adaptively created and moved to suit the current environment and needs. However, in shared settings, individual users’ workspaces are rarely aligned and can vary significantly in the number of monitors, available physical space, and workspace layout, creating inconsistencies between workspaces which may cause confusion and reduce collaboration. We present Desk2Desk, an optimization-based approach for remote collaboration in which the hybrid workspaces of two collaborators are fully integrated to enable immersive side-by-side collaboration. The optimization adjusts each user’s workspace in layout and number of shared monitors and creates a mapping between workspaces to handle inconsistencies between workspaces due to physical constraints (e.g. physical monitors). We show in a user study how our system adaptively merges dissimilar physical workspaces to enable immersive side-by-side collaboration, and demonstrate how an optimization-based approach can effectively address dissimilar physical layouts.
Keywords
Shared workspace, Remote collaboration, Mixed reality, Gesture retargeting
Discipline
Databases and Information Systems
Research Areas
Information Systems and Management; Software and Cyber-Physical Systems
Publication
Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (UIST 2024) : Pittsburgh, PA, USA, October 13-16
First Page
1
Last Page
15
Identifier
10.1145/3654777.3676339
Publisher
ACM Digital Library
City or Country
Pittsburgh, PA, USA
Citation
SIDENMARK, Ludwig; ZHANG, Tianyu; LABABIDI, Leen Al; LI, Jiannan; and GROSSMAN, Tovi.
Desk2Desk : Optimization-based mixed reality workspace integration for remote side-by-side collaboration. (2024). Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (UIST 2024) : Pittsburgh, PA, USA, October 13-16. 1-15.
Available at: https://ink.library.smu.edu.sg/sis_research/9748
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3654777.3676339
Comments
PDF provided by faculty