1. A processor-implemented method for reducing error between motion capture sessions, comprising: receiving a first and a second set of measured points, the first set of measured points from a first motion capture session and associated with a first set of facial skin markers of a target model in a first facial animation sequence of the target model, the second set of measured points from a second motion capture session and associated with a second set of facial markers of the target model in a second facial animation sequence of the target model, wherein each measured point is associated with a three-dimensional coordinate triple; computing, by a computer system including a processor, an optimal affine transformation function for reducing error between the motion capture sessions, wherein the computing includes, selecting an unprocessed point from the first set of measured points, the unprocessed point being a point of the first set of measured points that has not been used to compute any affine transformation function to minimize an error, selecting two nearby neighboring points of the unprocessed point, computing, as part of aligning the first set of measured points with the second set of measured points to reduce error between the motion capture sessions, an affine transformation function that minimizes an L 2 -norm error, and identifying the optimal affine transformation function from a set of computed affine transformation functions; and generating, by the computer system and utilizing the optimal affine transformation function, a head motion animation sequence.