AI

Abstract

We present an approach for generating face animations from large image collections of the same person. Such collections, which we call photobios, sample the appearance of a person over changes in pose, facial expression, hairstyle, age, and other variations. By optimizing the order in which images are displayed and crossdissolving between them, we control the motion through face space and create compelling animations (e.g., render a smooth transition from frowning to smiling). Used in this context, the cross dissolve produces a very strong motion effect; a key contribution of the paper is to explain this effect and analyze its operating range. The approach operates by creating a graph with faces as nodes, and similarities as edges, and solving for walks and shortest paths on this graph. The processing pipeline involves face detection, locating fiducials (eyes/nose/mouth), solving for pose, warping to frontal views, and image comparison based on Local Binary Patterns. We demonstrate results on a variety of datasets including time-lapse photography, personal photo collections, and images of celebrities downloaded from the Internet. Our approach is the basis for the Face Movies feature in Google’s Picasa.