Solid Texture Synthesis using Generative Adversarial Networks

Solid texture synthesis, as an effective way to extend 2D exemplar to a volumetric texture, exhibits advantages in numerous application domains. However, existing methods generally suffer from synthesis distortion due to the under-utilization of information. In this paper, we propose a novel approach for the solid texture synthesis based on generative adversarial networks(GANs), named STS-GAN, learning the distribution of 2D exemplars with volumetric operation in a feature-free manner. The multi-scale discriminators evaluate the similarities between patch exemplars and slices from generated volume, promoting the generator to synthesize realistic solid texture. Experimental results demonstrate that the proposed method can synthesize high-quality solid texture with similar visual characteristics to the exemplar.