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.
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Lan, R., Sun, H., Zhu, M. (2013). Text-Like Motion Representation for Human Motion Retrieval. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_10
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DOI: https://doi.org/10.1007/978-3-642-36669-7_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36668-0
Online ISBN: 978-3-642-36669-7
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