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

TL;DR

SwimXYZ is a large-scale synthetic dataset of swimming motions and videos designed to improve computer vision applications in aquatic sports, addressing the lack of labeled swimming datasets.

Contribution

The paper introduces SwimXYZ, a comprehensive synthetic dataset with millions of annotated frames and motion sequences, enabling advancements in swimming pose estimation and stroke analysis.

Findings

Effective use in swimming stroke clustering

Improved 2D pose estimation accuracy

Dataset availability for research community

Abstract

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.