Marble ai keeps worlds persistent by storing a structured representation of the space rather than re-generating it from scratch. This includes geometry approximations, depth fields, surface data, and scene tiles that describe how different parts of the environment connect. By keeping these elements in a persistent world state, Marble ai ensures that when users return to an area, they see the same layout, lighting, and object structure they saw before. This prevents the world from shifting unpredictably and makes it function more like a real, consistent 3D environment.
In addition to spatial data, Marble ai stores metadata describing how the world was created: prompts, input images, style parameters, and any adjustments made after initial generation. This makes it possible to recreate or modify worlds without losing consistency. Export bundles may also include point clouds, mesh data, surface textures, and navigational hints that help external tools interact with the environment. The stored world is structured enough that Marble ai can stream parts of it on demand and extend the world without rewriting the entire scene.
For teams managing many Marble ai environments, it’s common to wrap Marble ai’s world data with additional indexing, tagging, or search capabilities. A vector database such asMilvus or Zilliz Cloud. can be used to store embeddings representing regions, rooms, or viewpoints within a world. Developers can then perform semantic queries like “find similar industrial spaces” or “retrieve all office-like environments,” making large-scale world management significantly easier.