Text-Like motion representation for human motion retrieval

Authors:
Rongyi Lan;Huaijiang Sun;Mingyang Zhu
Affiliations:
School of Computer Science & Technology, Nanjing University of Science & Technology, Nanjing, Jiangsu, China;School of Computer Science & Technology, Nanjing University of Science & Technology, Nanjing, Jiangsu, China;School of Computer Science & Technology, Nanjing University of Science & Technology, Nanjing, Jiangsu, China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

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Abstract

Human motion capture (mo-cap) data has been increasingly applied in animation, movies and games in recent years due to its visual realism, and large amounts of them were accumulated. How to effectively search logically similar motions from large data repositories is a new challenge. The major limitation of existing methods is the semantic level of the features is not high enough to well distinguish different motion categories. In this paper, we propose a text-like motion representation based on key-pose extraction and hierarchical clustering (HC). This motion representation is easy to be extended or combined with topic models to obtain higher semantic-level features for motion retrieval. Our experiments demonstrate its scalability and performance in several applications, including motion retrieval and motion segmentation.