Where is the Fake? Patch-Wise Supervised GANs for Texture Inpainting
This addresses texture inpainting for computer vision applications, but it is incremental as it builds on existing GAN methods.
The paper tackles texture inpainting by proposing a patch-wise supervised GAN with a segmentor discriminator to improve local consistency, achieving state-of-the-art performance on the DTD dataset.
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.