Lilly Tech Systems
Advanced
Performance Optimization
Achieving smooth, low-latency real-time avatar animation requires careful optimization across the entire pipeline from camera capture to final rendering.
Latency Budget
| Pipeline Stage | Target Latency | Optimization Focus |
|---|---|---|
| Camera capture | < 16ms (60fps) | Camera settings, resolution |
| Face tracking AI | < 10ms | Model optimization, GPU inference |
| Parameter mapping | < 1ms | Efficient math, caching |
| Rendering | < 16ms | Avatar complexity, shader optimization |
| Total pipeline | < 50ms | Pipelining, async processing |
Model Optimization
- Quantization: Convert FP32 models to INT8 or FP16 for 2-4x speedup with minimal quality loss
- TensorRT: NVIDIA's inference optimizer for maximum GPU performance
- ONNX Runtime: Cross-platform inference engine with hardware acceleration
- Model pruning: Remove unnecessary parameters to reduce model size and inference time
- Knowledge distillation: Train smaller "student" models from larger "teacher" models
GPU Utilization
- Separate tracking and rendering onto different GPU streams
- Use async compute to overlap AI inference with rendering
- Monitor GPU memory to prevent swapping and stalls
- Consider dedicated GPU for tracking if running alongside games
Rendering Optimization
- LOD: Reduce avatar complexity when not in close-up
- Shader complexity: Use mobile-friendly shaders for real-time use
- Texture resolution: Balance quality vs memory for real-time performance
- Draw call batching: Minimize state changes per frame
Profiling first: Always profile before optimizing. Use GPU profilers (NSight, RenderDoc) to identify actual bottlenecks. Often the biggest gains come from unexpected places.