We introduce a method for automated temporal segmentation of human motion
data into distinct actions and compositing motion primitives based on
self-similar structures in the motion sequence. We use neighbourhood graphs for
the partitioning and the similarity information in the graph is further
exploited to cluster the motion primitives into larger entities of semantic
significance. The method requires no assumptions about the motion sequences at
hand and no user interaction is required for the segmentation or clustering. In
addition, we introduce a feature bundling preprocessing technique to make the
segmentation more robust to noise, as well as a notion of motion symmetry for
more refined primitive detection. We test our method on several sensor
modalities, including markered and markerless motion capture as well as on
electromyograph and accelerometer recordings. The results highlight our
system's capabilities for both segmentation and for analysis of the finer
structures of motion data, all in a completely unsupervised manner.

我们介绍了一种基于自相似结构的自动化时间分割人体运动数据的方法，并使用邻域图将运动基元聚类成更大的语义实体，实现了完全无监督的分割和分析运动数据的能力。