  We tackle the problem of texture inpainting where the input images are
textures with missing values along with masks that indicate the zones that
should be generated. Many works have been done in image inpainting with the aim
to achieve global and local consistency. But these works still suffer from
limitations when dealing with textures. In fact, the local information in the
image to be completed needs to be used in order to achieve local continuities
and visually realistic texture inpainting. For this, we propose a new segmentor
discriminator that performs a patch-wise real/fake classification and is
supervised by input masks. During training, it aims to locate the fake and thus
backpropagates consistent signal to the generator. We tested our approach on
the publicly available DTD dataset and showed that it achieves state-of-the-art
performances and better deals with local consistency than existing methods.
