人脸动画关键点最优化算法

Optimal Key Points for Facial Animation

Thin-shell变形算法基于一组控制点的位移计算所有顶点的位移,不同的控制点集合会得到不同的重构结果.为了使该变形算法更精确地重构人脸表情动画,基于该算法,提出一种最优控制点计算方法.首先将计算人脸变形最优控制点问题建模,以最小化thin-shell变形算法的重构误差为目标,提出一种自适应前后向贪婪策略学习最优化控制点,称为人脸动画关键点;通过引入边界约束、对称性约束以及多分辨率性,使得所提出的关键点具有通用性.实验结果证明,所提出的人脸动画关键点方法在仿真数据和真实数据上可以有效地获取关键控制点和重构人脸动画.

The thin-shell deformation algorithm calculates the displacements of all the vertices based on the displacements of control points. The reconstruction results may be different by using different sets of control points. An algorithm to extract optimal control points based on thin-shell method is proposed in order to reconstruct the facial motion data more accurately. First, this problem is formalized as minimizing the reconstruction. Then an adaptive forward-backward greedy strategy is proposed to learn the optimal control points which are called key points in our work. The boundary constraint, symmetry constraint and multi-resolution property are introduced and with these considerations, result key points are very robust. The experiments and comparisons demonstrate the proposed optimal key points could be used for effectively reconstructing facial animation.