臺灣博碩士論文加值系統

生動細緻的三維電腦動畫常需要專業美工人員花費大量的時間建置，越細緻的動畫表示需要越多的資料描述，而大量的描述資料對於儲存、傳送以及顯示動畫都會嚴重影響到效能問題。好的動畫分析與參數化可幫助我們壓縮動畫資料，因此動畫的分析與動畫參數化在三維電腦動畫研究領域中扮演著關鍵的角色。 本論文利用主值分析法(PCA)對於整個三維電腦動畫做初步的分析，透過分析的資料以及本論文所定義之運動相似度，進一步將動畫中的物體細切成多塊局部區塊，然後再將各個局部區塊獨立地再以主值分析法做第二次分析，由於各個局部區塊中每個構成點擁有相似的運動特徵，相對於第一次的主值分析法，第二次的主值分析法可以用更少量的資料來描述整個動畫，達到更高的壓縮比，而且如此的動畫參數化更能精確地描述物體在整個動畫中的運動特徵。

Vivid 3D animation often needs the professional artist spending lots of time to construct. The more detail the animation contains, the more information it shall store. The huge size of the information brings the problem of storing, transferring and displaying the animation. Fine analysis and parameterization of the animation will help to compress the data. Therefore, animation analysis and parameterization plays an important role in the area of computer animation. In this paper, our method is based on the Principle Component Analysis (PCA). We use PCA to analysis the entire sequence of animation in the first step. With the analyzed data and motion similarity we propose, we can decompose the animated model into many small regions. Then we apply PCA again on each region. Because each vertex in the same region has similar motion characteristic, the second pass PCA can reconstruct the animation with less data than that of the first pass PCA. The higher compression rate is expected and the parameterization of animation can describe the characteristics of the motion in the entire animation more precisely.

摘要.........................................................i
英文摘要....................................................ii
致謝.......................................................iii
目錄........................................................iv
表目錄.......................................................v
圖目錄......................................................vi
第一章 導論.................................................1
1.1 研究動機⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯1
1.2 研究內容架構與流程⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯2
1.3 研究之主要貢獻⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯5
第二章 相關研究⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯6
2.1 物體外型參數化空間與變形⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯6
2.2 三維電腦動畫壓縮⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯9
2.3 三維動作分析⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯11
2.4 網格切割⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯14
第三章 主值分析法運用於網格動畫分析⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯17
3.1 主值分析法簡介⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯17
3.2 主值分析法運用於網格動畫分析⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯19
3.2.1 指定參考關鍵畫格⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯19
3.2.2 正規化所有關鍵畫格⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯20
3.2.3 計算每個關鍵畫格與參考關鍵畫格的差異⋯⋯⋯⋯⋯⋯⋯⋯⋯23
3.2.4 計算⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯23
3.2.5 計算特徵向量與特徵值⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯23
3.2.6 以M個特徵向量參數化每個關鍵畫格資料⋯⋯⋯⋯⋯⋯⋯⋯⋯26
3.2.7 取K個特徵向量來重構整個網格動畫⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯27
第四章 網格動畫中物體之運動分析……………………………………29
4.1運動複雜度⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯29
4.2 運動相似維度⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯33
4.3 運動相似度⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯36
4.4 以運動相似度切割網格⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯36
4.4.1 區域成長⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯37
4.4.2 區域融合⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯38
4.5 區域邊界平滑化⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯40
第五章 動畫參數化⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯50
5.1 動作分析資料參數化⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯50
5.2 動畫壓縮比提升⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯51
第六章 實驗結果⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯53
6.1 初始主值分析結果⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯53
6.2 網格切割結果⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯59
6.3透過動作分析後的第二次主值分析結果⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯66
第七章 結論與未來展望⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯73
參考文獻⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯75
自述⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯78

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