Cohomological learning of periodic motion
Cohomological learning of periodic motion
复制标题
周期运动的上同调学习
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
D. Kragic
中科院分区：
文献类型：
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作者：
Mikael Vejdemo;Florian T. Pokorny;P. Skraba;D. Kragic
这项工作开发了一种新的框架，可以自动检测，参数化和插值周期性运动模式从运动捕捉序列。使用我们的框架，周期性运动，如步行和跑步步态或任何具有周期性结构的运动序列，如清洁，跳舞等，可以自动检测，而无需手动标记周期起点和终点。我们的方法构造了一个内在的运动参数化，计算速度快。使用这种参数化，我们能够生成原型周期性运动。此外，我们能够在各种运动之间进行插值，产生丰富的“混合”周期性动作。我们的方法是基于应用代数拓扑的思想。特别是，我们首次在图形应用程序中应用了一种新的基于持久上同调的方法，使我们能够恢复运动的圆坐标。我们还开发了一个合适的同伦概念，可用于周期性运动模式之间的插值。我们的框架直接适用于构建步行周期，用于使用运动图或状态机驱动的动画引擎来动画角色运动，并以每秒11.78帧的平均速度处理我们的示例。
This work develops a novel framework which can automatically detect, parameterize and interpolate periodic motion patterns obtained from a motion capture sequence. Using our framework, periodic motions such as walking and running gaits or any motion sequence with periodic structure such as cleaning, dancing etc. can be detected automatically and without manual marking of the period start and end points. Our approach constructs an intrinsic parameterization of the motion and is computationally fast. Using this parameterization, we are able generate prototypical periodic motions. Additionally, we are able to interpolate between various motions, yielding a rich class of ‘mixed’ periodic actions. Our approach is based on ideas from applied algebraic topology. In particular, we apply a novel persistent cohomology based method for the first time in a graphics application which enables us to recover circular coordinates of motions. We also develop a suitable notion of homotopy which can be used to interpolate between periodic motion patterns. Our framework is directly applicable to the construction of walk cycles for animating character motions with motion graphs or state machine driven animation engines and processed our examples at an average speed of 11.78 frames per secondGraphical abstract