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Style Animations Generated from Dynamic Model

Authors: Dengming Zhu; Zhaoqi Wang; Shihong Xia;

Style Animations Generated from Dynamic Model

Abstract

Recently, motion capture is widely used in the human animation. But it is still difficult to create new-style animations from the existing motion capture data. In this paper, we propose a novel technique to create the style animations from an existing motion sequence. Firstly, we use linear time-invariant system (LTI) to derive an explicit mapping between the high-dimensional motion capture data and the low-dimensional state variables. Secondly, new style animations are created within the state space. Only a few important keyframes need be modified through adjusting the low-dimensional style variables. The remaining frames of the original motion can be generated automatically. Finally, we design an effective algorithm to calculate the model parameters. Experimental results show that the generated style animations are natural and smooth.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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