Efficient Unsupervised Temporal Segmentation of Human Motion

TitleEfficient Unsupervised Temporal Segmentation of Human Motion
Publication TypeConference Proceedings
Year of Publication2014
AuthorsVögele A, Krüger B, Klein R
Conference Name2014 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Date Published07/2014
Conference LocationCopenhagen, Denmark
Project[Project Phase 2] Towards semantically steered navigation in shape spaces exemplified by rodent skull morphology in correlation to external attributes

This work introduces an efficient method for fully automatic temporal segmentation of human motion sequences and similar time series. The method relies on a neighborhood graph to partition a given data sequence into distinct activities and motion primitives according to self-similar structures given in that input sequence. In particular, the fast detection of repetitions within the discovered activity segments is a crucial problem of any motion processing pipeline directed at motion analysis and synthesis. The same similarity information in the neighborhood graph is further exploited to cluster these primitives into larger entities of semantic significance. The elements subject to this classification are then used as prior for estimating the same target values for entirely unknown streams of data. The technique makes no assumptions about the motion sequences at hand and no user interaction is required for the segmentation or clustering. Tests of our techniques are conducted on the CMU and HDM05 motion capture databases demonstrating the capability of our system handling motion segmentation, clustering, motion synthesis and transfer-of-label problems in practice - the latter being an optional step which relies on the preexistence of a small set of labeled data.