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arXiv · 2608.05971

PoseForge: Editable Pose Analytics for AI-Assisted Sports Coaching

Shuvam Swapnil Dash·Arpit Narechania
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Abstract

Athletic coaching increasingly relies on video analysis, yet raw footage lacks tools to quantify motion or simulate valid technique corrections. Drawing on formative interviews with eleven cricket experts (coaches, performance analysts, captains, and players), we introduce PoseForge, a visual analytics system that extracts 3D skeletal poses from single-camera sports videos for interactive movement analysis. In a cricket batting case study, PoseForge computes interpretable kinematic metrics such as feet gap and elbow angle, compares them against scientifically derived norms, and uses an AI coach to suggest targeted adjustments, presented visually and through natural-language feedback (e.g., "increase feet gap by 10 cm"). Users can directly modify poses via mouse interaction or natural-language instructions, with inverse kinematics maintaining anatomical plausibility and real-time updates of metrics and comparisons. An evaluation with the same eleven cricket experts found PoseForge effective for diagnosing movement issues and exploring corrective alternatives, highlighting its applicability in low-resource, academy, and grassroots coaching settings, while identifying opportunities for enhanced sport-specific metrics and longitudinal tracking. PoseForge is available as open-source software at https://github.com/DataVisards/PoseForge.

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BibTeXRIS

Shuvam Swapnil Dash, Arpit Narechania. 2026-08-06. PoseForge: Editable Pose Analytics for AI-Assisted Sports Coaching. https://arxiv.org/abs/2608.05971

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