Augmented Object Intelligence with XR-Objects
Name
3654777.3676379.pdf
Size
22.6 MB
Format
Adobe PDF
Checksum (MD5)
5b64bd01e355f2d8514e571ee1b84aa1
Author(s)
Dogan, Mustafa Doga
Gonzalez, Eric
Ahuja, Karan
Du, Ruofei
Cola?o, Andrea
Lee, Johnny
Gonzalez-Franco, Mar
Kim, David
Date Issued
October 13, 2024
Publisher
ACM|The 37th Annual ACM Symposium on User Interface Software and Technology
Citation
Dogan, Mustafa Doga, Gonzalez, Eric, Ahuja, Karan, Du, Ruofei, Cola?o, Andrea et al. 2024. "Augmented Object Intelligence with XR-Objects."
Version
Final published version
Abstract
Seamless integration of physical objects as interactive digital entities remains a challenge for spatial computing. This paper explores Augmented Object Intelligence (AOI) in the context of XR, an interaction paradigm that aims to blur the lines between digital and physical by equipping real-world objects with the ability to interact as if they were digital, where every object has the potential to serve as a portal to digital functionalities. Our approach utilizes real-time object segmentation and classification, combined with the power of Multimodal Large Language Models (MLLMs), to facilitate these interactions without the need for object pre-registration. We implement the AOI concept in the form of XR-Objects, an open-source prototype system that provides a platform for users to engage with their physical environment in contextually relevant ways using object-based context menus. This system enables analog objects to not only convey information but also to initiate digital actions, such as querying for details or executing tasks. Our contributions are threefold: (1) we define the AOI concept and detail its advantages over traditional AI assistants, (2) detail the XR-Objects system’s open-source design and implementation, and (3) show its versatility through various use cases and a user study.
Description
UIST ’24, October 13–16, 2024, Pittsburgh, PA, USA
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3654777.3676379
