The ultimate goal of the Meta-Object research is to enable a transformative reality-virtuality convergence by facilitating seamless interactions that transcend spatial and temporal barriers. This is to be achieved by implementing next-generation virtual objects that inherit the form, properties, and functions of their real-world counterparts, allowing for smooth synchronization, interaction, and sharing between the physical and virtual worlds. This will all be realized within a post-metaverse intelligent simulation platform through wearable AR/MR/VR devices.

This research aims to develop an open XR collaboration platform that induces high-quality immersion and social co-presence to enhance user collaboration quality. The platform is based on Scene Graph and utilizes digital twins to manage collaborative interactions of spatial models, static/dynamic objects, sound, hand gestures, and haptic elements through functions like creation, deletion, movement, and transformation. The study focuses on developing high-quality immersive elements and enhancing social co-presence by emphasizing various technologies. The research covers the composition and integration of 3D objects in the spatial domain, optimal alignment of scanned data and CAD models, sound reproduction techniques, application of hand gestures using reinforcement learning in a physics simulator, high-quality multi-modal haptic technology supporting wearable interfaces, avatar mesh-point cloud interaction, force-based interaction, deep learning models for 3D clothing reconstruction, adaptive spatial configuration between multiple VR clients and single AR hosts, CG avatar face and costume reconstruction with texture extraction, as well as the development of evaluation metrics and factors for enhancing social co-presence in XR.