3D Face Modeling From Monocular Video Sequences

In this chapter we present two algorithms for 3D face modeling from a monocular video sequence. The first method is based on Structure from Motion (SfM), while the second one relies on contour adaptation over time. The SfM based method incorporates statistical measures of quality of the 3D estimate into the reconstruction algorithm. The initial multi-frame SfM estimate is smoothed using a generic face model in an energy function minimization framework. Such a strategy avoids excessively biasing the final 3D estimate towards the generic model. The second method relies on matching a generic 3D face model to the outer contours of a face in the input video sequence, and integrating this strategy over all the frames in the sequence. It consists of an edge-based head pose estimation step, followed by global and local deformations of the generic face model in order to adapt it to the actual 3D face. This contour adaptation approach is able to separate the geometric subtleties of the human head from the variations in shading and texture, and it does not rely on finding accurate point correspondences across frames. Detailed experimental evaluation of both the methods along with reconstructed 3D models is presented.