AI Motion Capture Best Practices Advanced

This lesson covers production-grade best practices for AI motion capture, from optimizing capture conditions and reducing noise to cleaning up data and ensuring quality output for animation pipelines.

Capture Environment

• Lighting — Use bright, even lighting to minimize shadows that confuse pose estimation
• Background — A plain, contrasting background improves person detection accuracy
• Clothing — Avoid loose clothing that obscures body contours; fitted clothing works best
• Camera height — Position the camera at waist height, capturing the full body in frame
• Frame rate — Capture at the highest stable frame rate your hardware supports

Data Quality Pipeline

1. Capture — Record raw keypoint data with timestamps and confidence scores
2. Filter Low Confidence — Discard or interpolate keypoints below a confidence threshold (typically 0.5)
3. Temporal Smoothing — Apply One Euro or Kalman filtering to reduce jitter
4. Gap Filling — Interpolate missing frames using spline interpolation
5. Constraint Enforcement — Apply skeletal constraints to prevent impossible poses
6. Export — Convert to BVH/FBX with proper coordinate system transformation

Performance Optimization

Optimization	Impact
Reduce input resolution	2-4x speedup with moderate accuracy loss
Use GPU inference	3-10x faster than CPU for most models
Skip frames	Process every 2nd frame and interpolate for 2x throughput
ROI tracking	Only process the region around the detected person
Model quantization	Use INT8 or FP16 models for faster inference