SportsPose -- A Dynamic 3D sports pose dataset
Christian Keilstrup Ingwersen, Christian Mikkelstrup, Janus, N{\o}rtoft Jensen, Morten Rieger Hannemose, Anders Bjorholm Dahl

TL;DR
SportsPose is a large-scale, diverse 3D sports pose dataset capturing highly dynamic movements, enabling improved sports analytics and injury prevention research.
Contribution
We introduce SportsPose, a comprehensive 3D sports pose dataset with over 176,000 poses, and a new metric for local movement, advancing pose estimation in sports contexts.
Findings
Achieved a mean error of 34.5 mm, comparable to existing datasets.
Demonstrated that SportsPose contains more dynamic movements than Human3.6M and 3DPW.
Provided a publicly available dataset and code for further research.
Abstract
Accurate 3D human pose estimation is essential for sports analytics, coaching, and injury prevention. However, existing datasets for monocular pose estimation do not adequately capture the challenging and dynamic nature of sports movements. In response, we introduce SportsPose, a large-scale 3D human pose dataset consisting of highly dynamic sports movements. With more than 176,000 3D poses from 24 different subjects performing 5 different sports activities, SportsPose provides a diverse and comprehensive set of 3D poses that reflect the complex and dynamic nature of sports movements. Contrary to other markerless datasets we have quantitatively evaluated the precision of SportsPose by comparing our poses with a commercial marker-based system and achieve a mean error of 34.5 mm across all evaluation sequences. This is comparable to the error reported on the commonly used 3DPW dataset. We…
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Taxonomy
TopicsHuman Pose and Action Recognition · Sports Performance and Training · Stroke Rehabilitation and Recovery
