Recently, deformable face alignment is synonymous to the task of locating a set of 2D sparse landmarks in intensity images. Currently, discriminatively trained Deep Convolutional Neural Networks (DCNNs) are the state-of-the-art in the task of face alignment. DCNNs exploit large amount of high quality annotations that emerged the last few years. Nevertheless, the provided 2D annotations rarely capture the 3D structure of the face (this is especially evident in the facial boundary). That is, the annotations neither provide an estimate of the depth nor correspond to the 2D projections of the 3D facial structure. This paper summarises our efforts to develop (a) a very large database suitable to be used to train 3D face alignment algorithms in images captured "in-the-wild" and (b) to train and evaluate new methods for 3D face landmark tracking. Finally, we report the results of the first challenge in 3D face tracking "in-the-wild".