近年來，在資訊化越來越發達的時代，人們以經開始從二維邁向三維的時代了，加上最近的三維影像越來越廣泛的使用，例如像是在卡通、電玩、工程和電影等方面。而那些3D models的創建基本上都是利用一種叫作Motion capture的技術去補捉，而那些設備對於一般使用者太過於昂貴且難以取得，為了解決這問題，我們提出一個新的想法。利用兩台攝影機同步去拍攝一段影片，利用細化與追蹤演算法來達到關節點的取得，且把二維座標轉換成三維座標。 在此篇論文裡，我們結合了細化演算法與追蹤演算法，利用此兩個方法去抓取人體的關節點，再利用兩台攝影機所拍攝出的影像差異來算出每個關節點的三維座標，最後在把這些三維座標輸出至VRML裡，讓這些三維座標重建出三維化身。 Recently, 3D models are widely used in many areas such as cartoons, games, movie industry and engineering, etc. Presently, the most 3D Human kinematic motion models are constructed by optical devices such as 3D scanner, and Motion Capture. Generally, these devices are expensive and difficult to acquire, however. To avoid this problem, we propose a novel approach which based on the multi-cameras tracking to construct the 3D human avatars, since the motion capture devices are too expensive and difficult to acquire. An adaptive approach to skeleton detection is presented. First, we propose a novel method to combine thinning and tracking algorithm which can find the skeleton tracking points into three-dimensional avatars. The joints can be found by the thinning algorithm. And we also added tracking method to find the joints when the objects are disappeared. Finally, we reconstruct the 3D avatars by using multiple cameras to get the X, Y, and Z-axis coordinate.
