Model-based motion estimation for synthetic animations

Abstract

Maneesh Agrawala Andrew C. Beers Navin Chaddhay Computer Science Department yComputer Systems Laboratory Stanford University Stanford University ABSTRACT One approach to performing motion estimation on synthetic animations is to treat them as video sequences and use standard image-based motion estimation methods. Alternatively, we can take advantage of information used in rendering the animation to guide the motion estimation algorithm. This information includes the 3D movements of the objects in the scene and the projection transformations from 3D world space into screen space. In this paper we examine how to use this high level object motion information to perform fast, accurate block-based motion estimation for synthetic animations. The optical ow eld is a 2D vector eld describing the translational motion of each pixel from frame to frame. Our motion estimation algorithm rst computes the optical ow eld, based on the object motion information. We then combine the per-pixel motion information for a block of pixels to create a single 2D projective matrix that best encodes the motion of all the pixels in the block. The entries of the 2D matrix are determined using a least squares formulation. Our algorithms are more accurate and much faster in algorithmic complexity than many image-based motion estimation algorithms.