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.

翻译：固体质地合成是将2D外观扩展至体积质素的有效方法,它在许多应用领域显示出优势,然而,由于信息利用不足,现有方法一般会因合成扭曲而受到影响。在本文件中,我们提出了基于称为STS-GAN的基因性对抗网络(GANs)的固体质地合成新颖方法,以无特征的方式学习用体积操作的2D外光标的分布。多尺度歧视者评估了补丁外观器与生成体积的切片之间的相似性,促进生成器合成现实的固体质地质。实验结果表明,拟议的方法可以合成具有与外观特征相似的高质量固体质质。