GramGAN: Deep 3D Texture Synthesis From 2D Exemplars Supplemental Material

We first demonstrate the capability of our system to learn a continuous latent texture space when trained on a dataset consisting of diverse textures (Section 1). In Section 5, we tabulate the network architectures of the convolutional neural networks used in our experiments. First and last square in each strip correspond to resynthesized exemplars. Note how our result is closer to the exemplar texture. See Figure 7 (second row) and Figure 8 (third row) for exemplar images.