Generation-augmented latent navigation for continuous spatiotemporal zoom and rotation in immersive environments
A system and method for generation-augmented latent hyperspace navigation in spatiotemporal media using hierarchical and Lorentzian autoencoders. The system compresses media into latent representations while preserving geometric, temporal, and semantic relationships. A latent hyperspace manager organizes compressed data as geodesic trajectories, and a geodesic trajectory mapper computes navigation paths. Symbolic anchors provide persistent reference points, while spatiotemporal routing coordinates decisions across multiple scales. A strategy caching system preserves successful navigation patterns for reuse as procedural memory. A synthetic content generator including latent diffusion models, neural radiance fields, and context-aware refinement produces augmentation for continuous zoom, bidirectional traversal, and rotational reorientation. A user input interface and zoom controller enable interactive exploration and reconstruction, supporting applications in immersive media, visualization, and surveillance.
1 . A computer system for generated-augmented latent navigation in spatiotemporal media, 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 input data sets comprising video data organized as multi-dimensional tensors that preserve temporal and spatial relationships;
compress the input data sets into hierarchical and Lorentzian latent representations using a plurality of encoders configured to preserve tensor structure and causal flow;
establish a latent hyperspace managed by a latent hyperspace manager that organizes compressed representations as geodesic trajectories;
compute navigation paths through the latent hyperspace using a geodesic trajectory mapper;
position symbolic anchors at locations within the latent hyperspace to identify decision points, semantic boundaries, and temporal references;
implement spatiotemporal routing protocols to coordinate navigation across multiple temporal scales and semantic domains;
capture navigation patterns using a strategy caching system to extract core principles and generalize them into reusable navigation templates;
invoke a synthetic content generator comprising generative AI submodules configured to extrapolate beyond the resolution of the original input, including a latent diffusion module, a neural radiance field, and a detail synthesis generator, refined by a context-aware refiner;
perform continuous zoom and rotation of spatiotemporal content within the latent hyperspace while maintaining temporal consistency and semantic coherence; and
render reconstructed video and navigation insights as output.
2 . The computer system of claim 1 , wherein the symbolic anchors comprise at least one of decision points, semantic boundaries, navigation waypoints, or temporal references, each serving a cognitive landmark for navigation.
3 . The computer system of claim 1 , wherein the strategy caching system includes a strategy extractor configured to identify successful navigation patterns, a pattern generalizer configured to abstract reusable templates, and a context matcher configured to apply cached strategies in future navigation episodes.
4 . The computer system of claim 1 , wherein the synthetic content generator comprises a latent diffusion module to synthesize fine-grained detail, a neural radiance field module to enable rotational traversal and novel viewpoints, and a detail synthesis generator to generate pixel-level microstructure.
5 . The computer system of claim 4 , wherein the synthetic content generator further comprises a context-aware refiner including a scene processor, neural refiner, and temporal consistency validator configured to enforce semantic and temporal coherence across consecutive frames.
6 . The computer system of claim 1 , wherein the zoom and rotation operations are mediated by a zoom controller configured to translate user navigation inputs into latent traversal instructions executed by the latent hyperspace manager.
7 . The computer system of claim 6 , wherein the user input interface is configured to accept gestures, bounding-box selections, or control commands that specify a region of interest, a zoom level, or a rotational orientation for exploration of the spatiotemporal media.
8 . The computer system of claim 1 , wherein the geodesic trajectory mapper computes navigation paths by solving geodesic equations constrained by the manifold curvature of the latent space.
9 . The computer system of claim 1 , wherein symbolic anchors guide reconstruction such that zoom-in operations expand fine-grained details and zoom-out operations summarize higher-level structures, each aligned with anchor-defined semantic regions.
10 . The computer system of claim 1 , wherein the system outputs comprise reconstructed video content, synthesized novel perspectives, navigation recommendations, and trajectory-based strategic insights.
11 . A computer-implemented method for generation-augmented latent navigation in spatiotemporal media, comprising the steps of:
receiving a plurality of spatiotemporal media input data sets comprising video data organized as multi-dimensional tensors that preserve temporal and spatial relationships;
compressing the input data sets into hierarchical and Lorentzian latent representations using a plurality of encoders configured to preserve tensor structure and causal flow;
establishing a latent hyperspace managed by a latent hyperspace manager that organizes compressed representations as geodesic trajectories;
computing navigation paths through the latent hyperspace using a geodesic trajectory mapper;
positioning symbolic anchors at locations within the latent hyperspace to identify decision points, semantic boundaries, and temporal references;
implementing spatiotemporal routing protocols to coordinate navigation across multiple temporal scales and semantic domains;
capturing navigation patterns using a strategy caching system to extract core principles and generalize them into reusable navigation templates;
invoking a synthetic content generator comprising generative AI submodules configured to extrapolate beyond the resolution of the original input, including a latent diffusion module, a neural radiance field, and a detail synthesis generator, refined by a context-aware refiner;
performing continuous zoom and rotation of spatiotemporal content within the latent hyperspace while maintaining temporal consistency and semantic coherence;
rendering reconstructed video and navigation insights as output.
12 . The computer-implemented method of claim 11 , wherein the symbolic anchors comprise at least one of decision points, semantic boundaries, navigation waypoints, or temporal references, each serving a cognitive landmark for navigation.
13 . The computer-implemented method of claim 11 , wherein the strategy caching system includes a strategy extractor configured to identify successful navigation patterns, a pattern generalizer configured to abstract reusable templates, and a context matcher configured to apply cached strategies in future navigation episodes.
14 . The computer-implemented method of claim 11 , wherein the synthetic content generator comprises a latent diffusion module to synthesize fine-grained detail, a neural radiance field module to enable rotational traversal and viewpoints, and a detail synthesis generator to generate pixel-level microstructure.
15 . The computer-implemented method of claim 14 , wherein the synthetic content generator further comprises a context-aware refiner including a scene processor, neural refiner, and temporal consistency validator configured to enforce semantic and temporal coherence across consecutive frames.
16 . The computer-implemented method of claim 11 , wherein the zoom and rotation operations are mediated by a zoom controller configured to translate user navigation inputs into latent traversal instructions executed by the latent hyperspace manager.
17 . The computer-implemented method of claim 16 , wherein the user input interface is configured to accept gestures, bounding-box selections, or control commands that specify a region of interest, a zoom level, or a rotational orientation for exploration of the spatiotemporal media.
18 . The computer-implemented method of claim 11 , wherein the geodesic trajectory mapper computes navigation paths by solving geodesic equations constrained by the manifold curvature of the latent space.
19 . The computer-implemented method of claim 11 , wherein symbolic anchors guide reconstruction such that zoom-in operations expand fine-grained details and zoom-out operations summarize higher-level structures, each aligned with anchor-defined semantic regions.
20 . The computer-implemented method of claim 11 , wherein the system outputs comprise reconstructed video content, synthesized novel perspectives, navigation recommendations, and trajectory-based strategic insights.