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The Pose Model Stopped Inventing Missing Joints
A unified annotation scheme and structure-aware loss represented intact limbs, residual limbs and varied prostheses without forcing one anatomy onto all bodies.
Summary
A unified annotation scheme and structure-aware loss represented intact limbs, residual limbs and varied prostheses without forcing one anatomy onto all bodies.
ProPose addresses a benchmark bias toward able-bodied subjects by giving biological limbs, mechanical prostheses and physical absences one topological representation. A real-to-synthetic expansion pipeline adds scarce prosthetic cases, while ProLoss enforces dependencies within each limb so independent keypoint predictions do not hallucinate joints on mechanical structures. Reported classification accuracy for long-tail prosthetic joints improves by two to six percentage points without reducing coordinate localization. The work is a pose-estimation benchmark and model objective, not a clinical assessment system.
Why it matters
A unified annotation scheme and structure-aware loss represented intact limbs, residual limbs and varied prostheses without forcing one anatomy onto all bodies.
Limits and context
- A real-to-synthetic expansion pipeline adds scarce prosthetic cases, while ProLoss enforces dependencies within each limb so independent keypoint predictions do not hallucinate joints on mechanical structures.
- The work is a pose-estimation benchmark and model objective, not a clinical assessment system.
Key claims
A unified annotation scheme and structure-aware loss represented intact limbs, residual limbs and varied prostheses without forcing one anatomy onto all bodies.
Qualification: A real-to-synthetic expansion pipeline adds scarce prosthetic cases, while ProLoss enforces dependencies within each limb so independent keypoint predictions do not hallucinate joints on mechanical structures.
Evidence: source-2026-08-14-005
Sources
- arXiv preprint 2608.13047arXiv · primary research
Corrections
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