论文标题
对无标记运动捕获的3D人姿势估计算法的评论
A review of 3D human pose estimation algorithms for markerless motion capture
论文作者
论文摘要
人类的姿势估计是一个非常活跃的研究领域，它在机器人技术，娱乐或健康和体育科学等中的重要应用刺激了。卷积网络的进步触发了2D姿势估计的显着改善，导致现代3D无标记运动捕获技术达到平均每个关节误差20 mm。但是，随着方法的扩散，做出明智的选择变得越来越困难。在这里，我们回顾了过去五年中领先的人类姿势估计方法，重点是指标，基准和方法结构。我们提出了一种基于准确性，速度和鲁棒性的分类学，我们用来对方法进行分类，并为将来的研究提供方向。
Human pose estimation is a very active research field, stimulated by its important applications in robotics, entertainment or health and sports sciences, among others. Advances in convolutional networks triggered noticeable improvements in 2D pose estimation, leading modern 3D markerless motion capture techniques to an average error per joint of 20 mm. However, with the proliferation of methods, it is becoming increasingly difficult to make an informed choice. Here, we review the leading human pose estimation methods of the past five years, focusing on metrics, benchmarks and method structures. We propose a taxonomy based on accuracy, speed and robustness that we use to classify de methods and derive directions for future research.
