Solid texture synthesis (STS), an effective way to extend a 2D exemplar to a 3D solid volume, exhibits advantages in computational photography. However, existing methods generally fail to accurately learn arbitrary textures, which may result in the failure to synthesize solid textures with high fidelity. In this paper, we propose a novel generative adversarial nets-based framework (STS-GAN) to extend the given 2D exemplar to arbitrary 3D solid textures. In STS-GAN, multi-scale 2D texture discriminators evaluate the similarity between the given 2D exemplar and slices from the generated 3D texture, promoting the 3D texture generator synthesizing realistic solid textures. Finally, experiments demonstrate that the proposed method can generate high-fidelity solid textures with similar visual characteristics to the 2D exemplar.

翻译：立体纹理合成是将二维样本扩展至三维立体体积的有效方法，在计算摄影领域具有显著优势。然而，现有方法通常无法准确学习任意纹理，可能导致无法合成高保真度的立体纹理。本文提出一种基于生成对抗网络的新型框架STS-GAN，可将给定二维样本扩展至任意三维立体纹理。在STS-GAN中，多尺度二维纹理判别器评估给定二维样本与生成三维纹理切片之间的相似性，从而促使三维纹理生成器合成逼真的立体纹理。实验表明，该方法能生成与二维样本具有相似视觉特征的高保真度立体纹理。