论文标题
通过将Texton广播与噪声注入在stylegan-2中的噪声结合来迈向通用纹理综合
Towards Universal Texture Synthesis by Combining Texton Broadcasting with Noise Injection in StyleGAN-2
论文作者
论文摘要
我们通过将多尺度的Texton广播模块纳入StyleGAN-2框架中，提出了一种新的通用纹理合成方法。 Texton广播模块引入了感应偏见，从而产生了更广泛的纹理范围，从具有常规结构到完全随机的质地。为了训练和评估所提出的方法，我们构建了一个全面的高分辨率数据集，该数据集捕获了自然纹理的多样性以及每个感知均匀纹理中的随机变化。实验结果表明，所提出的方法的质量质地明显优于艺术状态。这项工作的最终目标是对纹理空间的全面理解。
We present a new approach for universal texture synthesis by incorporating a multi-scale texton broadcasting module in the StyleGAN-2 framework. The texton broadcasting module introduces an inductive bias, enabling generation of broader range of textures, from those with regular structures to completely stochastic ones. To train and evaluate the proposed approach, we construct a comprehensive high-resolution dataset that captures the diversity of natural textures as well as stochastic variations within each perceptually uniform texture. Experimental results demonstrate that the proposed approach yields significantly better quality textures than the state of the art. The ultimate goal of this work is a comprehensive understanding of texture space.
