Metavania F8VPS Ver.6.1
Creating the Metaverse Essential for the Era of Digital Transformation
Introduction
In the Version 6 update, we have enhanced city model utilization and communication platforms, focusing primarily on CityGML support and improved mobile operability. The new Version 6.1 update introduces expanded capabilities for a wide range of use cases—from academic research to event applications—under the core themes of visualizing scientific and technical data and elevating the user experience.
Digital Twins and Large Spatial Expression
Since the release of Version 6 on March 31, 2026, Metavania F8VPS significantly expanded its support for the digital twin sector. In addition to the CityGML import function introduced earlier, the following features have been implemented to further advance digital twin utilization:
Support of 3D Gaussian Splatting
Support for 3D rendering via Gaussian Splatting has been added. This technique generates high-quality 3D spaces at high speeds by distributing numerous Gaussian points throughout the environment.
Compared to photogrammetry and point cloud technologies, Gaussian Splatting reproduces real-world spaces with exceptional fidelity. By combining it with conventional 3D models, users can deliver high-quality digital twin data to their stakeholders.
Fig.1 Gaussian Splatting model source: Kiel - Kilia ［simonbethke/SuperSplat］（CC BY 4.0）
Improved camera control
Leveraging the intuitive operability developed in our 3DVR simulation software UC-win/Road, we have enhanced the camera functionality to optimize navigation and viewpoint control within large-scale environments. Pressing the SHIFT key increases movement speed, and right-clicking rotates the avatar around the clicked point.
Streamline visualization support
Support for VTK file import has been added, enabling advanced streamline visualization. VTK is a widely used data format in scientific computing and fluid dynamics analysis. This feature enables the intuitive representation of airflow and water flow simulation results within metaverse environments. Previously, simulation results were primarily reviewed using specialized analysis software; now, users can share and experience them within navigable 3D spaces. This capability makes it ideal for a wide range of applications, including presenting research findings, educational purposes, and supporting stakeholder consensus building.
Fig.2 Visualization of streamlines
Gamification
A new collection feature has been added. Users can acquire items by performing specific actions within the metaverse space and review their collected items on a dedicated page.
This enables the creation of gamified, user-participatory content, such as digital stamp rallies at exhibitions, achievement management for educational programs, and promoting exploration within tourism metaverses. By combining the joy of exploration and a sense of achievement with simple spatial browsing, this feature delivers a more engaging, long-term user experience.
Fig.3 Collection function used for Karuta Collection in Tamana Metaverse
Car parts collection feature in FORUM8 Rally Japan Metaverse
Future Development
We will continue to expand our features in Metavania F8VPS, focusing primarily on digital twin applications, multi-device support, and integration with AI technologies.
In the digital twin field, we plan to further expand functionality utilizing city models, introducing practical enhancements for professional use, such as data visualization within urban environments and the integrated display of analysis results. By supporting tsunami simulation visualization, we also aim to expand its utilization into the disaster management and mitigation fields. We support the utilization of simulations by research institutes and local governments by providing an environment where results can be experienced intuitively, rather than just analyzed as numerical data.
In addition, we are advancing multi-device support as a key priority in our development. We are enhancing display optimization technologies, such as LOD control and dynamic streaming, to ensure a seamless viewing experience in large-scale metaverse environments across both high-end PCs and lower-spec devices. We will establish a mechanism that dynamically switches 3D models to minimize rendering loads while displaying vast urban spaces, aiming to create a platform that expands digital twin accessibility across diverse user environments.
Regarding AI integration, we are developing features to streamline data creation for spatial construction and configuration. Furthermore, we are working to implement a metaverse AI assistant that handles facility guidance, information searches, and navigation to provide a more natural user experience.
