AthleticsPose: Authentic Sports Motion Dataset on Athletic Field and Evaluation of Monocular 3D Pose Estimation Ability

TL;DR

This paper introduces AthleticsPose, a new dataset of authentic sports motions for monocular 3D pose estimation, demonstrating its effectiveness and limitations in real-world sports analysis.

Contribution

The paper presents a novel sports motion dataset, AthleticsPose, and evaluates a 3D pose estimation model trained on it, highlighting the importance of authentic data for accurate sports motion analysis.

Findings

Model trained on AthleticsPose outperforms baseline by 75% MPJPE.

Estimation accuracy varies with camera view and subject scale.

Model captures individual knee angles but struggles with high-speed metrics.

Abstract

Monocular 3D pose estimation is a promising, flexible alternative to costly motion capture systems for sports analysis. However, its practical application is hindered by two factors: a lack of realistic sports datasets and unclear reliability for sports tasks. To address these challenges, we introduce the AthleticsPose dataset, a new public dataset featuring ``real'' motions captured from 23 athletes performing various athletics events on an athletic field. Using this dataset, we trained a representative 3D pose estimation model and performed a comprehensive evaluation. Our results show that the model trained on AthleticsPose significantly outperforms a baseline model trained on an imitated sports motion dataset, reducing MPJPE by approximately 75 %. These results show the importance of training on authentic sports motion data, as models based on imitated motions do not effectively transfer to real-world motions. Further analysis reveals that estimation accuracy is sensitive to camera view and subject scale. In case studies of kinematic indicators, the model demonstrated the potential to capture individual differences in knee angles but struggled with higher-speed metrics, such as knee-drive velocity, due to prediction biases. This work provides the research community with a valuable dataset and clarifies the potential and practical limitations of using monocular 3D pose estimation for sports motion analysis. Our dataset, code, and checkpoints are available at https://github.com/SZucchini/AthleticsPose.