  Recently, the Metaverse is becoming increasingly attractive, with millions of
users accessing the many available virtual worlds. However, how do users find
the one Metaverse which best fits their current interests? So far, the search
process is mostly done by word of mouth, or by advertisement on
technology-oriented websites. However, the lack of search engines similar to
those available for other multimedia formats (e.g., YouTube for videos) is
showing its limitations, since it is often cumbersome to find a Metaverse based
on some specific interests using the available methods, while also making it
difficult to discover user-created ones which lack strong advertisement. To
address this limitation, we propose to use language to naturally describe the
desired contents of the Metaverse a user wishes to find. Second, we highlight
that, differently from more conventional 3D scenes, Metaverse scenarios
represent a more complex data format since they often contain one or more types
of multimedia which influence the relevance of the scenario itself to a user
query. Therefore, in this work, we create a novel task, called
Text-to-Metaverse retrieval, which aims at modeling these aspects while also
taking the cross-modal relations with the textual data into account. Since we
are the first ones to tackle this problem, we also collect a dataset of 33000
Metaverses, each of which consists of a 3D scene enriched with multimedia
content. Finally, we design and implement a deep learning framework based on
contrastive learning, resulting in a thorough experimental setup.
