构建首个针对高强度体育动作的3D人体姿态基准数据集，提升运动分析精度。
AthletePose3D: A Benchmark Dataset for 3D Human Pose Estimation and Kinematic Validation in Athletic Movements
- 采集12类运动共165千个姿态，覆盖高速高加速动作
- 模型微调后平均关节误差从214mm降至65mm，降幅超69%
- 适合体育科学、康复研究及高动态场景下姿态估计研究者
人体姿态估计在计算机视觉与运动生物力学中至关重要，应用于体育科学、康复与生物力学研究。尽管单目3D姿态估计已取得进展，现有数据集难以捕捉竞技体育中的复杂高加速度动作。本文提出AthletePose3D，包含12类不同项目的体育动作，约130万帧图像与16.5万个体姿态，专门记录高速高加速的运动。我们在该数据集上评估了当前最优（SOTA）的单目2D与3D姿态估计算法，发现传统训练模型在运动动作上表现不佳。通过在AthletePose3D上微调，SOTA模型的平均关节位置误差（MPJPE）从214mm降至65mm，降幅超过69%。我们还通过波形分析验证了单目姿态估计的运动学准确性，结果显示关节角度估计相关性较强，但速度估计存在局限。本工作为单目姿态估计在高水平体育环境中的应用提供了全面评估，推动技术发展。数据集、代码与模型检查点已开源：https://github.com/calvinyeungck/AthletePose3D
原文摘要 · Abstract (English)
Human pose estimation is a critical task in computer vision and sports biomechanics, with applications spanning sports science, rehabilitation, and biomechanical research. While significant progress has been made in monocular 3D pose estimation, current datasets often fail to capture the complex, high-acceleration movements typical of competitive sports. In this work, we introduce AthletePose3D, a novel dataset designed to address this gap. AthletePose3D includes 12 types of sports motions across various disciplines, with approximately 1.3 million frames and 165 thousand individual postures, specifically capturing high-speed, high-acceleration athletic movements. We evaluate state-of-the-art (SOTA) monocular 2D and 3D pose estimation models on the dataset, revealing that models trained on conventional datasets perform poorly on athletic motions. However, fine-tuning these models on AthletePose3D notably reduces the SOTA model mean per joint position error (MPJPE) from 214mm to 65mm-a reduction of over 69%. We also validate the kinematic accuracy of monocular pose estimations through waveform analysis, highlighting strong correlations in joint angle estimations but limitations in velocity estimation. Our work provides a comprehensive evaluation of monocular pose estimation models in the context of sports, contributing valuable insights for advancing monocular pose estimation techniques in high-performance sports environments. The dataset, code, and model checkpoints are available at: https://github.com/calvinyeungck/AthletePose3D
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