Competitive figure skaters perform successful jumps with critical parameters, which are valuable for jump analysis in athlete training. Driven by recent computer vision applications, recovering 3D pose of figure skater to obtain the meaningful variables has become increasingly important. However, conventional works have suffered from getting 3D information based on the corresponding 2D information directly or leaving the specificity of sports out of consideration. Issues such as self-occlusion, abnormal pose, limitation of venue and so on will result in poor results. Motivated by these problems, this paper proposes a multi-task architecture based on a calibrated multi-camera system to facilitate jointly 3D jump pose of figure skater. The proposed methods consist of three key components: Likelihood distribution and temporal smoothness- based discrete probability points selection filter out the most valuable 2D information; Multi-perspective and combinations unification-based large-scale venue 3D reconstruction is proposed to deal with the multi-camera; multi-constraint-based human skeleton estimation decides the final 3D coordinate from the candidates. This work is proved can be applied to 3D animated display and motion capture of the figure skating competition. The success rate of the independent joint is: 93.38% of 70 mm error range, 92.57% of 50 mm error range and 91.55% of 30 mm error range.

竞技花样滑冰运动员使用关键参数进行成功的跳跃，这对于运动员训练中的跳跃分析很有价值。在最近的计算机视觉应用的推动下，恢复花样滑冰运动员的 3D 姿势以获得有意义的变量变得越来越重要。然而，传统作品存在直接基于相应的 2D 信息获取 3D 信息或忽略运动的特殊性的问题。自我遮挡、姿势异常、场地受限等问题都会导致效果不佳。受这些问题的启发，本文提出了一种基于校准多摄像头系统的多任务架构，以促进花样滑冰运动员的联合 3D 跳跃姿势。所提出的方法包括三个关键部分：基于似然分布和时间平滑的离散概率点选择过滤掉最有价值的二维信息；针对多摄像头的问题，提出了基于多视角和组合统一的大规模场地3D重建；基于多约束的人体骨骼估计从候选者中决定最终的 3D 坐标。这项工作被证明可以应用于花样滑冰比赛的3D动画展示和动作捕捉。独立关节的成功率为：70mm误差范围的93.38%、50mm误差范围的92.57%和30mm误差范围的91.55%。基于多约束的人体骨骼估计从候选者中决定最终的 3D 坐标。这项工作被证明可以应用于花样滑冰比赛的3D动画展示和动作捕捉。独立关节的成功率为：70mm误差范围的93.38%、50mm误差范围的92.57%和30mm误差范围的91.55%。基于多约束的人体骨骼估计从候选者中决定最终的 3D 坐标。这项工作被证明可以应用于花样滑冰比赛的3D动画展示和动作捕捉。独立关节的成功率为：70mm误差范围的93.38%、50mm误差范围的92.57%和30mm误差范围的91.55%。