Quaternion Space Sparse Decomposition for Motion Compression and Retrieval

dc.contributor.authorZhu, Mingyangen_US
dc.contributor.authorSun, Huaijiangen_US
dc.contributor.authorDeng, Zhigangen_US
dc.contributor.editorJehee Lee and Paul Kryen_US
dc.date.accessioned2014-01-29T08:00:50Z
dc.date.available2014-01-29T08:00:50Z
dc.date.issued2012en_US
dc.description.abstractQuaternion has become one of the most widely used representations for rotational transformations in 3D graphics for decades. Due to the sparse nature of human motion in both the spatial domain and the temporal domain, an unexplored yet challenging research problem is how to directly represent intrinsically sparse human motion data in quaternion space. In this paper we propose a novel quaternion space sparse decomposition (QSSD) model that decomposes human rotational motion data into two meaningful parts (namely, the dictionary part and the weight part) with the sparseness constraint on the weight part. Specifically, a linear combination (addition) operation in Euclidean space is equivalently modeled as a quaternion multiplication operation, and the weight of linear combination is modeled as a power operation on quaternion. Besides validations of the robustness, convergence, and accuracy of the QSSD model, we also demonstrate its two selected applications: human motion data compression and content-based human motion retrieval. Through numerous experiments and quantitative comparisons, we demonstrate that the QSSD-based approaches can soundly outperform existing state-of-the-art human motion compression and retrieval approaches.en_US
dc.description.seriesinformationEurographics/ ACM SIGGRAPH Symposium on Computer Animationen_US
dc.identifier.isbn978-3-905674-37-8en_US
dc.identifier.issn1727-5288en_US
dc.identifier.urihttps://doi.org/10.2312/SCA/SCA12/183-192en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.7 [Computer Graphics]en_US
dc.subjectThree Dimensional Graphics and Realismen_US
dc.subjectAnimationen_US
dc.titleQuaternion Space Sparse Decomposition for Motion Compression and Retrievalen_US
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