Generative Artificial Intelligence for Immersive Analytics

Chaoming Wang, Veronica Sundstedt, Valeria Garro

2025

Abstract

Generative artificial intelligence (GenAI) models have advanced various applications with their ability to generate diverse forms of information, including text, images, audio, video, and 3D models. In visual computing, their primary applications have focused on creating graphic content and enabling data visualization on traditional desktop interfaces, which help automate visual analytics (VA) processes. With the rise of affordable immersive technologies, such as virtual reality (VR), augmented reality (AR), and mixed reality (MR), immersive analytics (IA) has been an emerging field offering unique opportunities for deeper engagement and understanding of complex data in immersive environments (IEs). However, IA system development remains resource-intensive and requires significant expertise, while integrating GenAI capabilities into IA is still under early exploration. Therefore, based on an analysis of recent publications in these fields, this position paper investigates how GenAI can support future IA systems for more effective data exploration with immersive experiences. Specifically, we discuss potential directions and key issues concerning future GenAI-supported IA applications.

Paper Citation

in Harvard Style

Wang C., Sundstedt V. and Garro V. (2025). Generative Artificial Intelligence for Immersive Analytics. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP; ISBN 978-989-758-728-3, SciTePress, pages 938-946. DOI: 10.5220/0013308400003912

in Bibtex Style

@conference{ivapp25,
author={Chaoming Wang and Veronica Sundstedt and Valeria Garro},
title={Generative Artificial Intelligence for Immersive Analytics},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP},
year={2025},
pages={938-946},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013308400003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP
TI - Generative Artificial Intelligence for Immersive Analytics
SN - 978-989-758-728-3
AU - Wang C.
AU - Sundstedt V.
AU - Garro V.
PY - 2025
SP - 938
EP - 946
DO - 10.5220/0013308400003912
PB - SciTePress