Skeleton Crew: Inside Machine Vision

What the machine actually sees is almost nothing like what you see. When a volleyball AI watches a broadcast, it doesn't see athletes and a ball—it sees 16 skeletal dots per person and misses the ball 73% of the time. In this trilogy closer, hosts explore the gap between human perception and machine input, revealing how the representation you hand a model becomes its permanent ceiling.

00:00 - Introduction: The fundamentals trilogy conclusion
01:00 - The perception gap: What humans see vs. what machines see
03:00 - Skeleton detection: 16 skeletal points per athlete
06:00 - Ball detection: The 27% problem
10:00 - Court mapping: Converting pixels to meters
14:00 - Why representation shapes everything

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Sources & further reading:
• game-senser-fable repo — docs/podcast/LEARNING-CONTEXT.md §§1.3, 2, 5 (the
• input inventory; the flat-projection autopsy; the ranker domain-shift and leakage
• stories)
• game-senser-fable repo — reference/FINDINGS-2026-06-04.md (ball vs feet projection; the
• 19–26m measurement; per-skill breakdown)
• game-senser-fable repo — baseline/BASELINE_SCORES.md (25.4 vs 17.9; the degenerate
• sparse homography)
• game-senser-fable repo — JOURNAL.md iters 3–5 (pose candidates 99.6%; the ranker's two
• poisons and the scale-free fix)

This podcast episode was fully generated by AI — research, script, voices, and production. Built with Claude, Piper TTS, and automated pipeline tooling.