Markerless motion capture of man-machine interaction
Markerless motion capture of man-machine interaction
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DOI：
10.1109/cvpr.2008.4587520
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发表时间：
2008-06
期刊：
影响因子：
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通讯作者：
Bodo Rosenhahn;Christian Schmaltz;Thomas Brox;Joachim Weickert;Daniel Cremers;Hans-Peter Seidel
中科院分区：
文献类型：
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作者：
Bodo Rosenhahn;Christian Schmaltz;Thomas Brox;Joachim Weickert;Daniel Cremers;Hans-Peter Seidel
这项工作涉及运动员与运动装备互动的建模和无标记跟踪。与传统的无标记跟踪相比，由于额外的限制，与运动装备的交互伴随着关节运动的限制：虽然人类通常可以使用他们的所有关节，但与设备的交互会在某些关节之间施加耦合。执行骑车模式的骑车人就是一个例子：脚应该留在踏板上，而踏板又被限制为沿着 3D 空间中的圆形轨迹移动。在本文中，我们提出了一种无标记运动捕捉系统，该系统通过在姿势优化期间通过软约束对运动限制进行建模来考虑低维姿势流形。对两个不同模型（骑自行车者和滑雪板者）的实验证明了该方法的适用性。此外，我们还提供了具有挑战性的户外场景的运动捕捉结果，包括阴影和强烈的照明变化。
This work deals with modeling and markerless tracking of athletes interacting with sports gear. In contrast to classical markerless tracking, the interaction with sports gear comes along with joint movement restrictions due to additional constraints: while humans can generally use all their joints, interaction with the equipment imposes a coupling between certain joints. A cyclist who performs a cycling pattern is one example: The feet are supposed to stay on the pedals, which are again restricted to move along a circular trajectory in 3D-space. In this paper, we present a markerless motion capture system that takes the lower-dimensional pose manifold into account by modeling the motion restrictions via soft constraints during pose optimization. Experiments with two different models, a cyclist and a snowboarder, demonstrate the applicability of the method. Moreover, we present motion capture results for challenging outdoor scenes including shadows and strong illumination changes.