AI 中文总结
PoseForge是一款从单摄像头体育视频提取3D骨骼姿势的视觉分析系统，可计算运动学指标、对比科学标准并生成AI教练建议，经板球专家评估适用于多类教练场景。
AI 中文摘要
体育教练越来越依赖视频分析，但原始视频片段缺乏量化动作或模拟有效技术修正的工具。通过对11名板球专家（教练、表现分析师、队长及球员）的形成性访谈，我们提出PoseForge，这是一款视觉分析系统，可从单摄像头体育视频中提取3D骨骼姿势，用于交互式动作分析。在板球击球案例研究中，PoseForge计算可解释的运动学指标，如脚间距和肘角，将其与科学推导的标准进行比较，并利用AI教练建议针对性调整，以可视化形式和自然语言反馈呈现（例如“将脚间距增加10厘米”）。用户可通过鼠标交互或自然语言指令直接修改姿势，逆运动学技术可维持解剖学合理性，并实时更新指标与对比结果。对上述11名板球专家的评估显示，PoseForge可有效诊断动作问题并探索修正方案，突出其在资源匮乏环境、学院及基层教练场景中的适用性，同时指出在增强运动特定指标和纵向追踪方面的改进空间。PoseForge作为开源软件提供，网址为this https URL。
英文摘要
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.
Comments13 pages, 5 figures, 2 tables. To appear in IEEE VIS 2026
