Research papers on pose estimation show impressive accuracy numbers. Production apps on consumer devices tell a different story. Here's what I learned shipping real-time pose estimation to thousands of users across 22 sports. I built SportsReflector , an AI coaching app that analyzes athletic form using Apple's Vision framework on-device. The app runs pose estimation at 30fps during live sessions and frame-by-frame during video analysis. This article covers the gap between what the documentation promises and what actually works when real users point their iPhones at themselves in gyms, courts, and living rooms. The Model Options on iOS Apple gives you three paths for pose estimation on iOS: VNDetectHumanBodyPoseRequest (Vision framework) Extracts 19 body points. Runs on the Neural Engine. No custom model needed. This is what most developers should use. CreateML trained custom model Train your own pose model with labeled data. More control over which points you detect.…