现有的运动捕获方法大都存在运动捕获设备昂贵、演员运动受限等缺点，为此，提出了一种利用视觉技术从视频中提取人体运动的方法，并对其中的特片跟踪和三维运动序列恢复等关键技术进行了深入研究。基于人体模型的特征跟踪算法利用卡尔曼滤波和极线方程，能精确地跟踪比较大的人体运动；采用不共面的非线性定标模型和考虑运动不确定性的三维重建方法，能恢复逼真的三维人体骨架模型，实验结果验证了基于视频的运动捕获方法的可行性和有效性。

Motion capture has been one of the most promising technologies in character animation in recent years. However, most currently available motion capture approaches suffer from costly equipment and motion restriction caused by markers. To overcome these problems, a novel approach to extract 3D motion from video using vision technology is presented. The key issues, such as feature tracking and 3D reconstruction, are deeply studied. A model-based feature racking algorithm, which utilizes Kalman filter to predict coordinates of image features and epipolar line equation to aid tracking, is presented to track human motion with great variety. A non-coplanar nonlinear calibration model and a reconstruction approach taken uncertainty into consideration are applied to restore 3D human skeleton model. At last the experimental results and analysis of our VBHA (Video Based Human Animation) system demonstrate the feasibility and effectiveness of our approaches.