Multimodal latent hyperspace navigation incorporating spectral, spatial, temporal, and scale dimensions

Abstract

A system and method for multimodal latent hyperspace navigation that enables efficient compression and interactive exploration of spatiotemporal and spectral media content. The system encodes video data into a structured seven-dimensional hyperspace spanning spatial coordinates, temporal progression, viewing orientation, scale, and spectral wavelength using variational autoencoders that generate locally Lorentzian latent patches. Navigation through the hyperspace is achieved via learned geodesic transition functions guided by a latent-space metric tensor, while generative fill-in modules synthesize content for sparsely populated regions. The architecture supports real-time deployment on resource-constrained devices such as set-top boxes through efficient latent decoding and optional generative refinement. Applications include immersive film exploration with continuous zoom and viewpoint control, surveillance systems with anomaly detection capabilities, and hyperspectral environmental monitoring with real-time spectral analysis across multiple wavelength bands.