A Reference Model for the Analysis and Indexing of Metaverse Recordings for Information Retrieval

After the peak of the recent hype wave of interest surrounding the metaverse, virtual world applications remained in areas such as gaming, VR training, simulations, and collaboration. In this context, recordings are created which subsequently evolve into extensive collections that users may wish to access, search through, and retrieve items from. In order to facilitate searchability of metaverse recordings, it is necessary to adapt content analysis and indexing techniques to the specific characteristics of these recordings. This paper presents a reference model, the Processing Framework for Metaverse Recordings (PFMR), which details the phases of structural analysis, feature extraction, data mining, and feature fusion. The objective is to facilitate efficient retrieval of metaverse content. Our evaluation, based on a prototypical implementation, demonstrates the applicability and effectiveness of PFMR. This lays the groundwork for further integration of metaverse-specific content into Multimedia Information Retrieval systems. The evaluation of the 256 Metaverse Recording dataset shows that PFMRs’ domain-specific adaptability and integratability allows effective metaverse recording information retrieval for metaverse-specific features such as avatar detection, dialog mining, and toxicity classification.