Generative Scene Networks (GSN)

Train GSN on a dataset of indoor floor plans.

Generate 10,000 unique synthetic room configurations.

Simulate LiDAR and RGB-D sensors within the GSN-generated environments.

Export the synthetic sensor data to the robot's training pipeline.

Integrate GSN latent sampler into the game engine.

Define 'biomes' based on specific latent space clusters.

Generate real-time 3D backgrounds as the player moves into new zones.

Bake the resulting radiance fields into optimized meshes for runtime performance.

Input architectural constraints into the GSN latent space.

Interpolate between different design latents (e.g., modern vs. industrial).

Render 360-degree fly-throughs of the synthesized spaces.

Adjust parameters in real-time based on client feedback.

Load GSN model onto an edge-compute server.

Sync user headset tracking with the GSN camera parameters.

Render stereo views of the generated scene with zero texture crawling.

Stream frames to the VR device for an immersive experience.

Use GSN to synthesize specific hazardous road environments.

Augment scenes with lighting and weather variants.

Run the AV software through these 'hallucinated' dangerous scenarios.

Validate AV response in a safe, virtual 3D environment.

Capture a few photos of a modified physical room.

Fine-tune the GSN latent code to match the new visual data.

Generate a complete, navigable 3D model of the updated space.

Update the facility management database with the new 3D asset.

Generate a series of environment concepts using GSN latents.

Select the best concept and refine using the GSN high-res pipeline.

Render the final plates with matched lighting to the live-action footage.

Composite the actors into the view-consistent 3D environment.