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

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