We address the problem of how to reconstruct 3D face models from large unstructured photo collections, e.g., obtained by Google image search or from personal photo collections in iPhoto. This problem is extremely challenging due to the high degree of variability in pose, illumination, facial expression, non-rigid changes in face shape and reflectance over time and occlusions. In light of this extreme variability, no single reconstruction can be consistent with all of the images.

Instead, we define as the goal of reconstruction to recover a model that is locally consistent with the image set. That is, each local region of the model is consistent with a large set of photos, resulting in a model that captures the dominant trends in the input data for different parts of the face. Our approach leverages multi-image shading, but, unlike traditional photometric stereo approaches, allows for changes in viewpoint and shape. We optimize over pose, shape, and lighting in an iterative approach that seeks to minimize the rank of the transformed images. This approach produces high quality shape models for a wide range of celebrities from photos available on the Internet.
(Faculty: Seitz)