  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.
