AHCI RESEARCH GROUP
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Papers published in international journals,
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OUR RESEARCH
Scientific Publications
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2023
Vaidhyanathan, V.; Radhakrishnan, T. R.; López, J. L. G.
Spacify A Generative Framework for Spatial Comprehension, Articulation and Visualization using Large Language Models (LLMs) and eXtended Reality (XR) Proceedings Article
In: A., Crawford; N.M., Diniz; R., Beckett; J., Vanucchi; M., Swackhamer (Ed.): Habits Anthropocene: Scarcity Abundance Post-Mater. Econ. - Proc. Annu. Conf. Assoc. Comput. Aided Des. Archit., ACADIA, pp. 430–443, Association for Computer Aided Design in Architecture, 2023, ISBN: 979-898608059-8 (ISBN).
Abstract | Links | BibTeX | Tags: 3D data processing, 3D spaces, Architectural design, Built environment, C (programming language), Computational Linguistics, Computer aided design, Computer architecture, Data handling, Data users, Data visualization, Immersive media, Interior designers, Language Model, Natural languages, Spatial design, Three dimensional computer graphics, Urban designers, User interfaces, Visualization
@inproceedings{vaidhyanathan_spacify_2023,
title = {Spacify A Generative Framework for Spatial Comprehension, Articulation and Visualization using Large Language Models (LLMs) and eXtended Reality (XR)},
author = {V. Vaidhyanathan and T. R. Radhakrishnan and J. L. G. López},
editor = {Crawford A. and Diniz N.M. and Beckett R. and Vanucchi J. and Swackhamer M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192831586&partnerID=40&md5=996906de0f5ef1e6c88b10bb65caabc0},
isbn = {979-898608059-8 (ISBN)},
year = {2023},
date = {2023-01-01},
booktitle = {Habits Anthropocene: Scarcity Abundance Post-Mater. Econ. - Proc. Annu. Conf. Assoc. Comput. Aided Des. Archit., ACADIA},
volume = {2},
pages = {430–443},
publisher = {Association for Computer Aided Design in Architecture},
abstract = {Spatial design, the thoughtful planning and creation of built environments, typically requires advanced technical knowledge and visuospatial skills, making it largely exclusive to professionals like architects, interior designers, and urban designers. This exclusivity limits non-experts' access to spatial design, despite their ability to describe requirements and suggestions in natural language. Recent advancements in generative artificial intelligence (AI), particularly large language models (LLMs), and extended reality, (XR) offer the potential to address this limitation. This paper introduces Spacify (Figure 1), a framework that utilizes the generalizing capabilities of LLMs, 3D data-processing, and XR interfaces to create an immersive medium for language-driven spatial understanding, design, and visualization for non-experts. This paper describes the five components of Spacify: External Data, User Input, Spatial Interface, Large Language Model, and Current Spatial Design; which enable the use of generative AI models in a) question/ answering about 3D spaces with reasoning, b) (re)generating 3D spatial designs with natural language prompts, and c) visualizing designed 3D spaces with natural language descriptions. An implementation of Spacify is demonstrated via an XR smartphone application, allowing for an end-to-end, language-driven interior design process. User survey results from non-experts redesigning their spaces in 3D using this application suggest that Spacify can make spatial design accessible using natural language prompts, thereby pioneering a new realm of spatial design that is naturally language-driven. © ACADIA 2023. All rights reserved.},
keywords = {3D data processing, 3D spaces, Architectural design, Built environment, C (programming language), Computational Linguistics, Computer aided design, Computer architecture, Data handling, Data users, Data visualization, Immersive media, Interior designers, Language Model, Natural languages, Spatial design, Three dimensional computer graphics, Urban designers, User interfaces, Visualization},
pubstate = {published},
tppubtype = {inproceedings}
}