Scalable Organization of Collections of Motion Capture Data via Quantitative and Qualitative Analysis

This paper proposes a scalable method for organizing the collection of motion capture data for overview and exploration, and it mainly addresses three core problems, including data abstraction, neighborhood construction and data visualization. To alleviate the contradiction between limited visual space and the ever-increasing size of real-word datasets, hierarchical affinity propagation (HAP) is adopt to perform data abstraction on low-level pose features to generate multi-layers of data aggregations in consistent with coarse to fine abstraction levels of human cognition. To construct a meaningful neighborhood for user choosing a browsing path and positioning themselves, quartet analysis-based phylogenetic tree is created upon high-level pose features to produce more reliable neighbors for different aggregations of the specific abstraction level. To provide a convenient interactive environment for user navigation, a phylogenetic tree-centric visualization strategy in three-dimensional space is present. Experimental results on HDM05 motion capture dataset verify the effectiveness of the proposed method.