電腦動畫的普遍，加速了電腦圖學領域相關的研究。在現今的電腦動畫製作，主流的創造方式為自由的組合經由搜集而來的運動資料，如 Motion Capture 所取得的運動資料，應用這些真實的資料來創造出全新的電腦動畫，更能增加整個動畫的擬真性，而這些製作好的動畫資料，因為較細緻，所以三角形的數量高，並且每秒的畫格數量一樣非常高。

　　為了最佳化執行效率與儲存空間，將動畫資料應用在互動式環境上，本論文提出經由分析動態網格的運動來進行網格簡化的方法，讓簡化後的運動情形和外觀與原來相似。此外，我們提出使用基因演算法來進行關鍵畫格提取的方法，經由設計過的基因編碼與演化動作，讓系統自動從動態網格資料中提取出有意義的關鍵畫格，使動畫資料能更有效率的儲存、重覆使用與瀏覽。

　　Nowadays, the data-driven approach has become a dominant way to synthesize realistic animations. In general, motion capture is the most popular method to capture the realistic motion. However, those data captured in high frame rates and in fine resolution requires a great amount of storage space and a powerful graphics card for rendering animations.

　　In this paper, we propose a novel technique to simplify an animated mesh and to extract key-frames from the animation sequence. Our proposed methods can benefit many computer graphics applications including interactive environment, animation level of detail, and animation summarization for browsing and retrieval.

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