Hierarchical Lorentzian latent structures for immersive video compression and continuous exploration
A system and method are provided for immersive video compression and continuous exploration using hierarchical Lorentzian latent structures. Spatiotemporal media is encoded into hierarchical mini-Lorentzian representations using Lorentzian autoencoders operating at multiple scales (H macro , H meso , H micro ) that preserve tensor structure, temporal causality, and geometric relationships. The compressed representations are embedded in a Lorentzian manifold, where video content is organized as navigable geodesic trajectories. The hierarchy enables continuous multidimensional zoom operations, including fiber bundle expansion, semantic scale-shifting, and projection between scales, while maintaining semantic coherence and geometric consistency. Symbolic anchors, spatiotemporal routing protocols, and correlation-network-based restoration support intelligent navigation and high-fidelity decompression. Synthetic content is generated in context to extend exploration beyond original media boundaries. The architecture enables seamless transitions across spatial, temporal, spectral, and semantic dimensions for applications in immersive media, analysis, and visualization.
1 . A computer system for immersive video compression and continuous exploration, comprising:
a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that:
obtain a plurality of spatiotemporal media data organized as three-dimensional tensors with spatial and temporal dimensions preserved;
compress the data into hierarchical mini-Lorentzian representations using Lorentzian autoencoders operating at multiple scales that preserve tensor structure, temporal causality, and geometric relationships through three-dimensional convolutional operations;
embed the hierarchical mini-Lorentzian representations into a Lorentzian latent space having a geometric manifold structure in which temporal evolution of the media content is represented as navigable geodesic trajectories;
organize the Lorentzian latent space into hierarchical subspaces enabling continuous multidimensional zoom operations;
position symbolic anchors at semantically significant locations within the Lorentzian latent space; and
generate synthetic media content using a generative model conditioned on the manifold geometry and symbolic anchors to support exploration beyond original media boundaries while maintaining temporal coherence and geometric consistency.
2 . The computer system of claim 1 , wherein the Lorentzian autoencoders comprise hierarchical encoders and decoders operating at multiple scales from global scene structure to fine-grained spatial and temporal details.
3 . The computer system of claim 1 , wherein organizing into hierarchical subspaces comprises generating H macro for global scene composition, H meso for texture and edge features, and H micro for pixel-level detail and fiber bundle expansion.
4 . The computer system of claim 1 , wherein continuous multidimensional zoom comprises zoom-in operations that expand into high-resolution fiber bundles and zoom-out operations that project to coarse-scale subspaces while preserving semantic coherence.
5 . The computer system of claim 1 , wherein computing optimal navigation paths comprises solving geodesic equations subject to Lorentzian metric constraints.
6 . The computer system of claim 1 , wherein the symbolic anchors are associated with semantic labels from a symbolic vocabulary and are integrated with multimodal metadata.
7 . The computer system of claim 1 , wherein spatiotemporal routing protocols implement multi-scale temporal coordination with time horizons ranging from milliseconds for frame-level decisions to minutes for session-level planning.
8 . A computer-implemented method for immersive video compression and continuous exploration, comprising the steps of:
obtaining spatiotemporal media data organized as three-dimensional tensors with spatial and temporal dimensions preserved;
compressing the data into hierarchical mini-Lorentzian representations using Lorentzian autoencoders operating at multiple scales that preserve tensor structure, temporal causality, and geometric relationships through three-dimensional convolutional operations;
embedding the hierarchical mini-Lorentzian representations into a Lorentzian latent space having a geometric manifold structure in which temporal evolution of the media content is represented as navigable geodesic trajectories;
organizing the Lorentzian latent space into hierarchical subspaces enabling continuous multidimensional zoom operations;
positioning symbolic anchors at semantically significant locations within the Lorentzian latent space;
generating synthetic media content using a generative model conditioned on the manifold geometry and symbolic anchors to support exploration beyond original media boundaries while maintaining temporal coherence and geometric consistency.
9 . The computer-implemented method of claim 8 , wherein the Lorentzian autoencoders comprise hierarchical encoders and decoders operating at multiple scales from global scene structure to fine-grained spatial and temporal details.
10 . The computer-implemented method of claim 8 , wherein organizing into hierarchical subspaces comprises generating H macro for global scene composition, H meso for texture and edge features, and H micro for pixel-level detail and fiber bundle expansion.
11 . The computer-implemented method of claim 8 , wherein continuous multidimensional zoom comprises zoom-in operations that expand into high-resolution fiber bundles and zoom-out operations that project to coarse-scale subspaces while preserving semantic coherence.
12 . The computer-implemented method of claim 8 , wherein computing optimal navigation paths comprises solving geodesic equations subject to Lorentzian metric constraints.
13 . The computer-implemented method of claim 8 , wherein the symbolic anchors are associated with semantic labels from a symbolic vocabulary and are integrated with multimodal metadata.
14 . The computer-implemented method of claim 8 , wherein spatiotemporal routing protocols implement multi-scale temporal coordination with time horizons ranging from milliseconds for frame-level decisions to minutes for session-level planning.
15 . The computer-implemented method of claim 8 , wherein generating synthetic content comprises applying at least one of latent diffusion models, neural radiance fields, and detail synthesis generators trained on domain-specific video datasets.