As 3D Gaussian Splatting (3DGS) emerges as a leading approach for novel view synthesis and scene reconstruction, its potential in digital asset creation has gained significant attention. An increasing number of asset libraries based on GS are being established. However, generating physics-based dynamic assets remains a time-consuming and expertise-intensive task, especially for non-experts. In this paper, we propose LIVE-GS, a highly realistic Virtual Reality (VR) system powered by Large Language Models (LLMs), which enables rapid creation of dynamic Gaussian assets and real-time VR interactions. To inform our system design, we conducted interviews to examine challenges faced by current GS-based VR systems and the specific demands of users. Based on these insights, we employed GPT-4o to analyze key physical properties of objects that significantly impact user interactions, ensuring physics-based interactions in VR align with real-world phenomena. A key innovation of LIVE-GS is its ability to predict reasonable parameters in just 10 seconds from static Gaussian assets while maintaining high-quality VR interactions. To validate our approach, we invited participants experienced in physical simulation to manually adjust physical parameters, providing a baseline for comparison in both asset quality and authoring efficiency. We also conducted a comprehensive user study to evaluate system usability and user satisfaction. Experimental results demonstrate that LIVE-GS, leveraging LLMs' scene understanding capabilities, can achieve efficient physical scene creation and natural interactions without requiring manual design or annotation.
In this special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG), we are pleased to present the most highly reviewed papers from the 33rd IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR 2026), held March 21-25, 2026, in Daegu, Korea.
Welcome to the 15th IEEE Transactions on Visualization and Computer Graphics (TVCG) special issue dedicated to IEEE Virtual Reality and 3D User Interfaces. This volume presents 160 full papers that were selected for and showcased at the IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR 2026), held in Daegu, Korea, from March 21 to 25, 2026.
We propose a spatial affordance-aware affine transformation method between heterogeneous spaces for continuous multi-object matching in shared Mixed Reality (MR) spaces. While previous redirection and spatial mapping approaches utilize physical objects and walkable areas, a critical gap remains in enabling continuous mapping between dissimilar physical environments that supports both precise object alignment and seamless locomotion in a shared space. Our method structurally segments heterogeneous spaces into interaction zones and constructs affine patches based on object adjacency and facing configuration, enabling continuous correspondence. We evaluate our method using a dataset of paired dissimilar spaces and demonstrate that, unlike conventional grid-based methods, our approach achieves broader spatial alignment and richer object matching. The results show that our method can serve as an effective mapping framework for shared environments requiring semantic continuity and structural coherence across diverse real-world spaces.
This research investigates using virtual agents for facilitating collaboration in immersive virtual meetings. We especially focus on how proactive engagement of virtual agents can improve user experience in discussions held in immersive virtual meetings. We developed an immersive virtual meeting system with a virtual agent that proactively provides feedback on discussions based on the agenda, meeting time left, and talk time of each participants. Findings from a user study comparing passive and proactive virtual agent behaviours suggest proactive behaviours can help facilitating an immersive virtual meeting yet could be distracting, needing further investigation to provide more nuanced and relevant intervention.
