LIVE-GS: LLM Powers Interactive VR by Enhancing Gaussian Splatting

We propose LIVE-GS, a highly realistic interactive Gaussian splatting system in VR environments powered by LLM. Our pipeline supports reconstructions and physically-based interactions in VR, integrating object-aware reconstruction, GPT-assisted inpainting, and a computationally efficient simulation framework. To enhance scene understanding, we prompt GPT-4o to analyze the physical properties of objects in the scene, thereby guiding physical simulations to align with real-world phenomena. Our experimental results demonstrate that with the assistance of LLM’s understanding and scene enhancement, our VR system can support complex and realistic interactions without requiring additional manual design or annotation.

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Publication Details

Published
2025-03-08
DOI
https://doi.org/10.1109/vrw66409.2025.00263
Citations
2
Primary Topic
Computer Graphics and Visualization Techniques
Type
article
Field-Weighted Citation Impact
7.08
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article

LIVE-GS: LLM Powers Interactive VR by Enhancing Gaussian Splatting

Nianchen Deng, Xubo Yang, Suhang Wei, Hao‐Tian Mao et al.
2 citations
Computer Graphics and Visualization Techniques
7.08
article

LIVE-GS: LLM Powers Interactive VR by Enhancing Gaussian Splatting

Nianchen Deng, Xubo Yang, Suhang Wei, Hao‐Tian Mao, Zhijie Xu, Youli Quan
article en
2 citations

Abstract

We propose LIVE-GS, a highly realistic interactive Gaussian splatting system in VR environments powered by LLM. Our pipeline supports reconstructions and physically-based interactions in VR, integrating object-aware reconstruction, GPT-assisted inpainting, and a computationally efficient simulation framework. To enhance scene understanding, we prompt GPT-4o to analyze the physical properties of objects in the scene, thereby guiding physical simulations to align with real-world phenomena. Our experimental results demonstrate that with the assistance of LLM’s understanding and scene enhancement, our VR system can support complex and realistic interactions without requiring additional manual design or annotation.

Shanghai Jiao Tong University (CN), Shanghai Artificial Intelligence Laboratory (CN)
Openalex Percentile: Top 4%
Computer Graphics and Visualization Techniques
7.08
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