Metaverse Retrieval: Finding the Best Metaverse Environment via Language

In recent years, the metaverse has sparked an increasing interest across the globe and is projected to reach a market size of more than \1000B by 2030. This is due to its many potential applications in highly heterogeneous fields, such as entertainment and multimedia consumption, training, and industry. This new technology raises many research challenges since, as opposed to the more traditional scene understanding, metaverse scenarios contain additional multimedia content, such as movies in virtual cinemas and operas in digital theaters, which greatly influence the relevance of the metaverse to a user query. For instance, if a user is looking for Impressionist exhibitions in a virtual museum, only the museums that showcase exhibitions featuring various Impressionist painters should be considered relevant. In this paper, we introduce the novel problem of text-to-metaverse retrieval, which proposes the challenging objective of ranking a list of metaverse scenarios based on a given textual query. To the best of our knowledge, this represents the first step towards understanding and automating cross-modal tasks dealing with metaverses. Since no public datasets contain these important multimedia contents inside the scenes, we also collect and annotate a dataset which serves as a proof-of-concept for the problem. To establish the foundation for it, we implement and analyze several solutions based on deep learning, whereas to promote transparency and reproducibility, we will publicly release their source code and the collected data.