在本篇論文中，我們提出了一個基於拉邦動作分析( Laban Movement Analysis , LMA)的動作風格合成法。從一組具特殊風格的動作中，我們研究其拉邦動作分析因子之特性並加以參數化，使得這組參數若加諸於中性的動作資料中便能合成具有本特殊風格的動作。我們採用了人類動作觀察(human movement observation)領域中的拉邦動作分析理論-特別是在勁(Effort)與形(Shape)這個部分。這個方法提供了我們可調節的數值化參數，並且可以讓我們從動作品質的觀點來控制動作風格。我們也探討高階的情緒語意與拉邦動作分析之間的關係。最後我們實做了一個動作合成系統，核心是採用動作擷取資料驅動的動力學模型。使用者可以依其需求，輸入中性的動作資料與一組情緒參數或拉邦動作分析參數，然後本系統便能夠根據這些參數產生出具有使用者所要求之風格的動作資料。由於核心採用的是動力學的模型，所以也可以克服動作經過修改而破壞了原本物理限制條件的問題。

In this thesis, we will present a motion style synthetic method based on Laban Movement Analysis (LMA). We will study the characteristic of LMA factors and parameterize it from a set of motions with special style. The set of parameters will add special styles to the neutral motion data. We will advocate the domain of human movement observation, especially LMA and its Effort and Shape components, to archive adjustable numerical parameters and control of motion quality aspects. We will also discuss the relation between high level emotion semantics and LMA. Finally, we will implement a motion synthesizer with motion capture data driven dynamics model kernel. The user could depend on his demand, input neutral motion data and a set of emotion parameters or LMA parameters to produce the motion data and style he requires according to the parameters he sets up.

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