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Technologies play an increasingly important role in sports and become a realcompetitive advantage for the athletes who benefit from it. Among them, the useof motion capture is developing in various sports to optimize sportinggestures. Unfortunately, traditional motion capture systems are expensive andconstraining. Recently developed computer vision-based approaches also strugglein certain sports, like swimming, due to the aquatic environment. One of thereasons for the gap in performance is the lack of labeled datasets withswimming videos. In an attempt to address this issue, we introduce SwimXYZ, asynthetic dataset of swimming motions and videos. SwimXYZ contains 3.4 millionframes annotated with ground truth 2D and 3D joints, as well as 240 sequencesof swimming motions in the SMPL parameters format. In addition to making thisdataset publicly available, we present use cases for SwimXYZ in swimming strokeclustering and 2D pose estimation.

SwimXYZ: A large-scale dataset of synthetic swimming motions and videos