基于拉普拉斯算子的人脸表情克隆优化方法

针对人脸克隆表情的真实性和表情重建的效率，提出了一种基于运动捕捉数据的人脸表情克隆新方法。使用运动捕捉设备捕捉人脸六种基本表情数据，并对数据进行归一化处理，使其位于同一区间范围，保证克隆表情数据的有效性；在表现人脸表情的关键区域选取41个特征点，通过采用凸权值的拉普拉斯变形算法将人脸表情转移到目标模型上，很好保留了人脸表情的细节，克服了均一权值保真度低和余切权值计算不稳定的问题。实验结果表明，该方法合成的克隆表情真实、自然，人脸表情克隆的效率得到了显著提高。

Facial expression cloning optimization method based on Laplace operator

In view of the reality of facial expression cloning and efficiency of expression reconstruction, a novel method based on motion capture data is proposed. After capturing the data of six fundamental expressions, it normalizes these data to make them in the same range. Then 41 points are chosen in critical areas of facial expression and it gets cloning expression using Laplace deformation algorithm with convex weight which can preserve the details of facial expression to avoid the low fidelity of uniform weights and unstable calculation of cotangent weights. Experimental results show that this method can generate realistic and natural expression animations and the efficiency of facial expression cloning is improved significantly.