Visual-Interactive Semi-Supervised Labeling of Human Motion Capture Data
Visual-Interactive Semi-Supervised Labeling of Human Motion Capture Data
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DOI：
10.2352/issn.2470-1173.2017.1.vda-387
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发表时间：
2017-01
期刊：
影响因子：
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通讯作者：
J. Bernard;Eduard Dobermann;Anna Vögele;Björn Krüger;J. Kohlhammer;D. Fellner
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文献类型：
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作者：
J. Bernard;Eduard Dobermann;Anna Vögele;Björn Krüger;J. Kohlhammer;D. Fellner
大型多变量时间序列数据的表征和抽象往往对有效性或有效性提出了挑战。以人体运动捕捉数据为例，在创建紧凑的解决方案方面存在挑战，这些解决方案仍然以有意义的方式重新定位(fl)ECT语义和运动学。提出了一种基于视觉交互的人体运动捕捉数据半监督标注方法。用户能够为数据分配标签，这些标签随后可用于将多变量时间序列表示为运动类别序列。该方法结合了在视觉交互标记过程中支持用户的多个视图。视觉指导概念通过传播支持性算法模型的结果进一步简化了标记过程。运动捕捉数据到事件间隔序列的抽象允许纵览和按需细节可视化，即使对于大型和异类数据集合也是如此。用于扩展和改进标注的候选数据的引导选择闭合了半监督工作flow的反馈回路。我们以视觉交互学习和人体运动合成为例，展示了该方法在两个使用场景中的有效性和fi有效性。
The characterization and abstraction of large multivariate time series data often poses challenges with respect to effectiveness or efficiency. Using the example of human motion capture data challenges exist in creating compact solutions that still re-flect semantics and kinematics in a meaningful way. We present a visual-interactive approach for the semi-supervised labeling of human motion capture data. Users are enabled to assign labels to the data which can subsequently be used to represent the multivariate time series as sequences of motion classes. The approach combines multiple views supporting the user in the visual-interactive labeling process. Visual guidance concepts further ease the labeling process by propagating the results of supportive algorithmic models. The abstraction of motion capture data to sequences of event intervals allows overview and detail-on-demand visualizations even for large and heterogeneous data collections. The guided selection of candidate data for the extension and improvement of the labeling closes the feedback loop of the semi-supervised workflow. We demonstrate the effectiveness and the efficiency of the approach in two usage scenarios, taking visual-interactive learning and human motion synthesis as examples.