Technologies play an increasingly important role in sports and become a real competitive advantage for the athletes who benefit from it. Among them, the use of motion capture is developing in various sports to optimize sporting gestures. Unfortunately, traditional motion capture systems are expensive and constraining. Recently developed computer vision-based approaches also struggle in certain sports, like swimming, due to the aquatic environment. One of the reasons for the gap in performance is the lack of labeled datasets with swimming videos. In an attempt to address this issue, we introduce SwimXYZ, a synthetic dataset of swimming motions and videos. SwimXYZ contains 3.4 million frames annotated with ground truth 2D and 3D joints, as well as 240 sequences of swimming motions in the SMPL parameters format. In addition to making this dataset publicly available, we present use cases for SwimXYZ in swimming stroke clustering and 2D pose estimation.

翻译：技术手段在体育领域发挥着日益重要的作用，已成为运动员获取竞争优势的关键因素。其中，动作捕捉技术在各类体育项目中被广泛应用以优化运动姿态。然而，传统动作捕捉系统成本高昂且具有诸多限制。近年来基于计算机视觉的方法在游泳等特定运动中面临困难，主要源于水下环境的特殊性。造成性能差距的原因之一，是缺乏标注完善的游泳视频数据集。为解决这一问题，我们提出了SwimXYZ——一个合成游泳运动与视频数据集。该数据集包含340万帧标注了真实2D/3D关节点的图像，以及240段采用SMPL参数格式存储的游泳运动序列。除公开数据集外，我们还展示了SwimXYZ在游泳划水聚类和2D姿态估计中的典型应用案例。