Exploring the potential of StyleGAN for modeling high-quality and diverse digital wood textures: Towards advancements in the wood industry

Abstract

Wood texture is pivotal in maximizing the value of trees and timber resources. Consequently, digital modeling and simulation of wood texture have become essential in wood science and industry. Therefore, researching simulation modeling techniques for digital wood texture has significant implications for advancing wood science and industry. This paper introduces a novel approach to modeling and simulating wood texture, focusing on the perspective of deep learning. The proposed method explored the viability of utilizing the StyleGAN model to generate digital wood texture. Fréchet Inception Distance(FID), visual Turing tests, and 1/f fluctuation spectrum analysis were used to evaluate the effectiveness of the digital wood texture models. Additionally, various control techniques were discussed for generating digital wood texture using StyleGAN models. The experimental results strongly indicated that the StyleGAN model exhibits robust capabilities in generating digital wood texture, as evidenced by an FID index of 13. Moreover, the visual Turing tests revealed that professional identification was similar to random guessing, while the fluctuation spectrum analysis demonstrated pixel distribution frequencies similar to those observed in real wood textures. Furthermore, in terms of controlling the simulation of digital wood texture, the StyleGAN model demonstrated remarkable abilities surpassing any previous models based on physical modeling. By fine-tuning truncation parameters and employing network layer mixing techniques, the model could generate the wood texture of various tree species, demonstrating outstanding generalization capabilities.