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SpaceBlender: Creating Context-Rich Collaborative Spaces Through Generative 3D Scene Blending

Numan, Nels; Rajaram, Shwetha; Kumaravel, Balasaravanan Thoravi; Marquardt, Nicolai; Wilson, Andrew D; (2024) SpaceBlender: Creating Context-Rich Collaborative Spaces Through Generative 3D Scene Blending. In: Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology. (pp. pp. 1-25). ACM: Pittsburgh, PA, USA. Green open access

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Abstract

There is increased interest in using generative AI to create 3D spaces for Virtual Reality (VR) applications. However, today’s models produce artificial environments, falling short of supporting collaborative tasks that benefit from incorporating the user’s physical context. To generate environments that support VR telepresence, we introduce SpaceBlender, a novel pipeline that utilizes generative AI techniques to blend users’ physical surroundings into unified virtual spaces. This pipeline transforms user-provided 2D images into context-rich 3D environments through an iterative process consisting of depth estimation, mesh alignment, and diffusion-based space completion guided by geometric priors and adaptive text prompts. In a preliminary within-subjects study, where 20 participants performed a collaborative VR affinity diagramming task in pairs, we compared SpaceBlender with a generic virtual environment and a state-of-the-art scene generation framework, evaluating its ability to create virtual spaces suitable for collaboration. Participants appreciated the enhanced familiarity and context provided by SpaceBlender but also noted complexities in the generative environments that could detract from task focus. Drawing on participant feedback, we propose directions for improving the pipeline and discuss the value and design of blended spaces for different scenarios.

Type: Proceedings paper
Title: SpaceBlender: Creating Context-Rich Collaborative Spaces Through Generative 3D Scene Blending
Event: UIST '24: The 37th Annual ACM Symposium on User Interface Software and Technology
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3654777.3676361
Publisher version: http://dx.doi.org/10.1145/3654777.3676361
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10200407
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