Co-occurrence Based Texture Synthesis

We model local texture patterns using the co-occurrence statistics of pixel values. We then train a generative adversarial network, conditioned on co-occurrence statistics, to synthesize new textures from the co-occurrence statistics and a random noise seed. Co-occurrences have long been used to measure similarity between textures. That is, two textures are considered similar if their corresponding co-occurrence matrices are similar. By the same token, we show that multiple textures generated from the same co-occurrence matrix are similar to each other. This gives rise to a new texture synthesis algorithm. We show that co-occurrences offer a stable, intuitive and interpretable latent representation for texture synthesis. Our technique can be used to generate a smooth texture morph between two textures, by interpolating between their corresponding co-occurrence matrices. We further show an interactive texture tool that allows a user to adjust local characteristics of the synthesized texture image using the co-occurrence values directly.