We consider data-driven approaches for universal texture modeling via generative adversarial networks and inversion methods. We investigate the properties of the learned representation spaces and demonstrate that a strong link between texture analysis and synthesis is the key to successful texture modeling. First, we visit the problem of texture synthesis...
We consider data-driven approaches for universal texture modeling via generative adversarial networks and inversion methods. We investigate the properties of the learned representation spaces and demonstrate that a strong link between texture analysis and synthesis is the key to successful texture modeling. First, we visit the problem of texture synthesis...