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 cross-dissolving 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 our research is to explain this motion effect and analyze its operating range. The approach operates by creating a graph with faces as nodes and similarities as edges and then 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 comparing images 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.