The total (gradient) variation is a regularizer which has been widely used in inverse problems arising in image processing, following the pioneering work of Rudin, Osher and Fatemi. In this talk, I will describe the structure the solutions to the total variation regularized variational problems when one has a finite number of measurements. First, I will present a general representation principle for the solutions of convex problems, then I will apply it to the total variation by describing the faces of its unit ball. It is a joint work with Claire Boyer, Antonin Chambolle, Yohann De Castro, Frédéric de Gournay and Pierre Weiss.