Solid Texture Synthesis using Generative Adversarial Networks

Solid texture synthesis, as an effective way to extend 2D texture to 3D solid texture, exhibits advantages in numerous application domains. However, existing methods generally suffer from synthesis distortion due to the underutilization of texture information. In this paper, we proposed a novel neural network-based approach for the solid texture synthesis based on generative adversarial networks, namely STS-GAN, in which the generator composed of multi-scale modules learns the internal distribution of 2D exemplar and further extends it to a 3D solid texture. In addition, the discriminator evaluates the similarity between 2D exemplar and slices, promoting the generator to synthesize realistic solid texture. Experiment results demonstrate that the proposed method can synthesize high-quality 3D solid texture with similar visual characteristics to the exemplar.