the third column the retrieved texture information. The architecture of the network and the training protocol is described in detail in the supplementary material Section \[app:implementation\_details\]. As we can see in the examples illustrated in Figure \[fig:texture\_descomposition\], an accurate low resolution texture representation of the face can be achieved in general, and visible artifact are observed only in the regions where the depth is discontinuous (see for example, the regions illustrated at the bottom of Figure \[fig:texture\_descomposition\]).

![Examples of the facial texture recovered from the image with the projected pattern. The first column, shows the input image (denoted as $I$ in Algorithm \[alg:facial\_embedding\]). The second column shows the ground truth, and the third column the texture recovered by the network $I_{rgb}$. This examples are from the test set and the images associated to these subjects were never seen during the training phase. []{data-label="fig:texture_descomposition"}](figures/texture_recovery.jpg){width=".6\columnwidth"}

Figure \[fig:depth\_gradient\_descomposition\] illustrates the ground truth and the retrieved depth gradient (again, for random samples from the test set). To estimate the 3D information, we feed to a different branch of convolutional layers the gray version of the input image. These layers are fully described in the supplementary material Table \[tab:DescompositionNetwork\]. A gray input image is considered 