基于信心的多体动力学优化,用于无标记的动作捕捉:概念验证
Anaïs Chaumeil1, Pierre Puchaud2, Antoine Muller1
1Univ Eiffel, Univ Lyon 1, LBMC UMR T_9406, Lyon, France.
概括
这项研究引入了一种用于无标记运动捕捉的新方法,该方法利用2D信心热图. 这种方法提高了准确性和稳定性,特别是当缺少关键点时,改进了3D人体姿势估计.
科学领域:
- 生物力学 生物力学
- 计算机视觉 计算机视觉
- 动作捕捉是一种动作捕捉技术.
背景情况:
- 无标记运动捕捉通常从2D关键点三角化3D点,并使用多体动力学优化 (MKO).
- 现有的方法往往忽视来自姿势估计网络的二维信心热图,将稳定性限制在缺失的数据上.
研究的目的:
- 开发和评估一种新的MKO方法,其中包括2D信心热图.
- 提高无标记运动捕捉的稳定性和准确性,特别是在关键点被封闭或丢失的场景中.
主要方法:
- 模拟的2D信心热图作为2D高斯函数.
- 通过将生物机械模型投射到摄像机平面中,最大化了模拟的信心总和.
- 评估了使用双摄像头设置的坐立,步行和手动物料处理任务的方法.
主要成果:
- 热图的高斯式建模显示出高有效性,与离散地图相比,平均绝对差异为0.011.
- 基于信心的MKO产生了与传统基于距离的方法相比较的3D关节位置和角度.
- 基于信任的方法成功计算了100%的,克服了阻碍基于距离的方法在89.3%的中计算的隐蔽.
结论:
- 将二维信心热图纳入MKO显著提高了无标记运动捕捉的稳定性.
- 提出的方法在具有挑战性的条件下特别有效,例如相机设置稀疏和缺少关键点.
- 这种方法为无标记运动捕捉提供了有希望的进步,最大限度地利用姿势估计网络的信息.
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