Marble ai processes videos by taking advantage of the multiple viewpoints and motion present in the footage. A still image provides only a single perspective, so the system must infer a large amount of missing geometry. With video, Marble ai can observe parallax, motion cues, and occlusion changes to estimate depth more accurately. Each frame becomes a data point in reconstructing the 3D environment, and the system aligns these frames to build a consistent spatial map. As a result, video-based worlds are often more structurally accurate and contain more fine-grained detail.
The workflow generally involves sampling key frames, estimating depth across the sequence, and merging these partial geometries into a unified representation. Marble ai also uses the video to fill in areas that would be impossible to infer from a single still image—corners, hallways, or areas partially blocked in earlier frames. This multi-frame reconstruction stage allows Marble ai to reduce ambiguity and produce environments that hold up better under close inspection or complex navigation paths.
From a system-design perspective, videos generate more data and therefore require more bandwidth, storage, and preprocessing. If you want to manage a large library of video-derived worlds, storing embeddings of representative frames in a vector database such asMilvus or Zilliz Cloud. can be very helpful. It enables semantic search across video-based environments, such as retrieving “all retail stores with wide aisles” or “all classrooms with similar layouts,” even when the source material differs in lighting or camera motion.