AI System for Automatic 3D VR Scene Generation
Creating VR environments traditionally — photogrammetry or manual modeling — takes weeks. Neural network methods allow generating complete 3D scenes from text description, 2D images or partial geometry. Result — accelerated prototyping and ability to create infinitely variable environments.
Technology Stack
Text-to-Scene:
- SceneScape / Set-the-Scene — diffusion-based 3D scene generation from description
- PanoGen for 360° panoramic environments (fast method for skybox-first iterations)
- LERF (Language Embedded Radiance Fields) — NeRF with semantic understanding
Image-to-3D / Reconstruction:
- Gaussian Splatting (3DGS) — rapid reconstruction from 20–100 photos, real-time rendering
- Instant-NGP (NVIDIA) — fast NeRF training in minutes
- MVSNet / DUSt3R — multi-view stereo reconstruction
Procedural Population:
- Blender Python API for automatic scene population with objects
- Instance segmentation + replacement: detect template objects and replace with 3D assets
VR Optimization:
- Automatic LOD-generation (Instant Meshes + custom decimation)
- Occlusion culling optimization
- Lightmap baking automation
Pipeline
Weeks 1–3: Define scene types (interior/exterior, realistic/stylized). Test reconstruction methods on client examples.
Weeks 4–8: Build generation pipeline. Configure asset library for population. Develop VR optimization post-processing.
Weeks 9–12: Unreal Engine or Unity integration. Test performance on target devices.
Metrics
| Method | Generation Time | Quality | Application |
|---|---|---|---|
| Gaussian Splatting (50 photos) | 5–15 min | Photorealistic | Real objects and spaces |
| Text-to-Scene | 2–10 min | Medium-high | Fantasy/sci-fi environments |
| PanoGen (360°) | 30–60 sec | High for skybox | Rapid prototyping |
| Manual+AI Population | 1–3 h | High | Detailed interiors |
Final scenes exported in formats compatible with Quest 3 (OpenXR/GL), Unreal Engine 5 (UAsset), Unity (Prefab), WebXR (glTF 2.0).