AI System for AR/VR
AR/VR without AI — static experience. With AI — dynamic environment that responds to user, adapts complexity, generates content in real time. We design AI layer on top of existing XR platforms or build new products with AI at foundation.
Architectural Patterns
Adaptive Environments: ML controller responds to user behavior: movement speed, gaze direction (eye tracking), pauses, stress level (heart rate via wearables). Environment adapts in real time — lighting, object density, narrative pace.
Procedural Content Generation:
- Infinite terrain via GAN-based height map generation
- Object population with semantic rules (forest = trees + bushes + rocks in correct proportions)
- NeRF-based scene reconstruction from 2D photos for rapid VR environment creation
Intelligent Avatars / NPCs:
- LLM-based dialogue with NPC (local Llama 3 8B for real-time without lag)
- Emotion recognition via facial tracking → adaptive NPC response
- Spatial audio with AI mixing (FMOD + ML controller)
Computer Vision for AR:
- Plane detection + semantic segmentation (ARKit/ARCore + custom NN)
- Object recognition and tracking for interactive overlays
- Hand tracking (MediaPipe) for gesture-based interaction
Technology Stack
Unity (ML-Agents, Barracuda) and Unreal Engine 5 (NeuralNetworkInference plugin) as main platforms. OpenXR for cross-platform compatibility. ONNX Runtime for inference directly in engine.
Development Pipeline
Weeks 1–3: XR requirements analysis. Determine AI use cases with greatest impact. Platform and target device selection (Quest 3, Vision Pro, HoloLens 2, WebXR).
Weeks 4–9: AI module development: generative content, adaptive systems, NPC intelligence. XR platform integration.
Weeks 10–14: Performance optimization for target devices. VR requires stable 72–120 fps — latency budget extremely limited. Model quantization, ONNX export, on-device inference.
Weeks 15–18: User testing. Motion sickness prevention via user movement analysis. Final optimization.
Performance Constraints
| Device | Inference Budget | Recommended Models |
|---|---|---|
| Meta Quest 3 | 5–10 TOPS | MobileNet, EfficientDet, TFLite |
| Apple Vision Pro | 38 TOPS (Neural Engine) | CoreML, BNNS |
| PC VR (RTX 4080) | ~60 TOPS | ONNX, any <7B parameters |
| HoloLens 2 | 4 TOPS | Quantized MobileNet, TFLite |
Project Examples
Industrial AR trainer with AI assistant (40% training time reduction), VR therapy with adaptive exposure system (validated in 3 clinics), AR warehouse navigation with real-time object detection.