Unsupervised motion capture data segmentation based on topic model,Computer Animation and Virtual Worlds

当前位置： X-MOL 学术 › Comput. Animat. Virtual Worlds › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Unsupervised motion capture data segmentation based on topic model
Computer Animation and Virtual Worlds ( IF 0.9 ) Pub Date : 2021-06-16 , DOI: 10.1002/cav.2005
Xiaoyan Hu ₁ , Xizhao Bao ₁ , Shunbo Xie ₂ , Guoli Wei ₁

Affiliation

In this paper, we propose an unsupervised motion segmentation method based on topical model borrowed from Natural Language Processing. We apply hierarchical clustering on motion dataset to obtain a list of representative poses to constitute motion 'vocabulary'. By doing so, motion capture data can be viewed as text which comprises a sequence of motion words. We use sliding window to generate a sequence of motion documents (with overlap between consecutive motion documents). Then we use Sparse Topical Coding (STC) model to extract sparse topical codes of motion documents and conduct spectral clustering to get motion segmentations. Silhouette coefficient is used to determine the value of K (number of motion types). The results of experiments show that our method can segment motions with a very high accuracy. Our method has a strong generalization ability that also performs well on motion data which is captured by different subjects, with various motion types, even though they are from different motion dataset (HDM05 in our experiment).

中文翻译：

基于主题模型的无监督运动捕捉数据分割

在本文中，我们提出了一种基于从自然语言处理中借用的主题模型的无监督运动分割方法。我们在运动数据集上应用层次聚类，以获得构成运动“词汇”的代表性姿势列表。通过这样做，运动捕捉数据可以被视为包括运动词序列的文本。我们使用滑动窗口来生成一系列运动文档（连续运动文档之间有重叠）。然后我们使用稀疏主题编码（STC）模型提取运动文档的稀疏主题代码并进行谱聚类以获得运动分割。剪影系数用于确定K的值（运动类型的数量）。实验结果表明，我们的方法可以以非常高的精度分割运动。我们的方法具有很强的泛化能力，即使它们来自不同的运动数据集（我们实验中的 HDM05），它也能很好地处理由不同对象捕获的具有各种运动类型的运动数据。

更新日期：2021-07-12

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11