## Model-based motion estimation for synthetic images

Maneesh Agrawala,
Andrew C. Beers, and
Navin Chaddha

*Accepted to
ACM Multimedia 95*

### 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 flow field is a 2D vector field describing the
translational motion of each pixel from frame to frame. Our motion
estimation algorithm first computes the optical flow field, 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 most image-based motion estimation
algorithms.

### Other information available:

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