Abstract
A long standing problem in computer graphics and animation is the production of synthetic computer graphics models whose appearance, movement and behaviour are visually indistinguishable from the real world. This thesis addresses the problem of reconstructing visually realistic computer graphics models of real people using multiple camera views. A model-based computer vision algorithm is introduced to reconstruct the shape and appearance of a person in an arbitrary pose viewed in a multiple camera studio. Current techniques for multiple view reconstruction address the problem of general scene recovery. These non model-based approaches can fail to accurately reconstruct shape and appearance in the presence of visual ambiguities. The techniques also provide no structure to edit or reuse the captured content in computer animation. The primary novel contributions in this research work are 1) a shape constrained deformable model formulation to match a generic model to shape information in multiple view silhouettes in the presence of visual ambiguities; and 2) a model-based multiple view reconstruction algorithm to recover a model that matches appearance across multiple views to sub-pixel accuracy. Model-based multiple view reconstruction of people is evaluated and results are presented for the reconstruction of shape and appearance of people in an arbitrary pose. The recovered models provide an accurate shape representation for a person and a visual appearance approaching the quality of the original camera images. The models also provide a consistent structured representation for the editing, synthesis and transmission of 3D content in computer graphics and animation.