Regularized bundle-adjustment to model heads from image sequences without calibration data

We address the structure-from-motion problem in the context of head modeling from video sequences for which calibration data is not available. This task is made challenging by the fact that correspondences are difficult to establish due to lack of texture and that a quasi-euclidean representation is required for realism. We have developed an approach based on regularized bundle-adjustment. It takes advantage of our rough knowledge of the head's shape, in the form of a generic face model. It allows us to recover relative head-motion and epipolar geometry accurately and consistently enough to exploit a previously-developed stereo-based approach to head modeling. In this way, complete and realistic head models can be acquired with a cheap and entirely passive sensor, such as an ordinary video camera, with minimal manual intervention. We chose to demonstrate and evaluate our technique mainly in the context of head-modeling. We do so because it is the application for which all the tools required to perform the complete reconstruction are available to us. We will, however, argue that the approach is generic and could be applied to other tasks, such as body modeling, for which generic facetized models exist.