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- W173826853 abstract "In this paper, we propose a method for locomotion analysis and synthesis based on motion manifold learning. The motions like walking and running can be considered as lying on low dimensional intrinsic structures because arms and legs operate in a coordinated way. Based on this assumption, we extract low dimensional embeddings from high dimensional motion capture data with Isomap. The dynamic motion features and motion segments are revealed by analyzing these embeddings. Then we synthesize new locomotion by mapping low dimensional representations into the high dimensional space. The learned motion manifold provides a more direct way to visualize, analyze and synthesize locomotion in our framework." @default.
- W173826853 created "2016-06-24" @default.
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- W173826853 date "2007-07-01" @default.
- W173826853 modified "2023-10-18" @default.
- W173826853 title "MANIFOLD LEARNING BASED LOCOMOTION ANALYSIS AND SYNTHESIS" @default.
- W173826853 doi "https://doi.org/10.1142/9789812709677_0221" @default.
- W173826853 hasPublicationYear "2007" @default.
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