The problem of reconstructing the 3D structure of objects from multiple or single images has been studied for more than three decades. In this tutorial, we will focus on the particular case of reconstructing a 3D model of the human face from a single image by means of the 3D Morphable Model (3DMM) technique.
We will start by introducing the problem of 3D dense registration of point clouds, which is still a challenging and open problem and represents a necessary step for building a training set for the 3DMM. Then, we will present the optimization techniques used to estimate the 3DMM parameters from a single image, and illustrate how the fitting permits the reconstruction of an approximated and smooth foundation shape of the face. In the second part, we will introduce the recent advances in the field obtained by deploying the power of deep learning techniques. Even though the diffusion of such in the 3D vision field has had a slower expansion, deep learning techniques are currently being applied with promising results to 3D data represented as depth images as well. Novel deep learning based techniques for fitting a 3DMM and reconstructing face images will be finally introduced.
The tutorial will be presented at 3DV 2018, Verona, Italy on Saturday September the 8th. The detailed program will be defined soon