We introduce TexTile, a novel differentiable metric to quantify the degree upon which a texture image can be concatenated with itself without introducing repeating artifacts (i.e., the tileability). Existing methods for tileable texture synthesis focus on general texture quality, but lack explicit analysis of the intrinsic repeatability properties of a texture. In contrast, our TexTile metric effectively evaluates the tileable properties of a texture, opening the door to more informed synthesis and analysis of tileable textures. Under the hood, TexTile is formulated as a binary classifier carefully built from a large dataset of textures of different styles, semantics, regularities, and human annotations.Key to our method is a set of architectural modifications to baseline pre-train image classifiers to overcome their shortcomings at measuring tileability, along with a custom data augmentation and training regime aimed at increasing robustness and accuracy. We demonstrate that TexTile can be plugged into different state-of-the-art texture synthesis methods, including diffusion-based strategies, and generate tileable textures while keeping or even improving the overall texture quality. Furthermore, we show that TexTile can objectively evaluate any tileable texture synthesis method, whereas the current mix of existing metrics produces uncorrelated scores which heavily hinders progress in the field.

翻译：我们提出TexTile，一种新颖的可微度量方法，用于量化纹理图像在不引入重复伪影（即可平铺性）的情况下与自身拼接的程度。现有可平铺纹理合成方法侧重于整体纹理质量，但缺乏对纹理固有重复性特性的显式分析。相比之下，我们的TexTile度量可有效评估纹理的可平铺特性，为更智能的可平铺纹理合成与分析开辟了道路。TexTile本质上是基于包含不同风格、语义、规律性及人工标注的大规模纹理数据集精心构建的二分类器。该方法的核心在于：对基线预训练图像分类器进行一系列架构改进以克服其测量可平铺性时的缺陷，同时设计专门的数据增强与训练策略以提升鲁棒性与准确性。实验表明，TexTile可嵌入包括扩散策略在内的多种先进纹理合成方法，在保持甚至提升整体纹理质量的同时生成可平铺纹理。进一步地，我们证明TexTile能客观评估任意可平铺纹理合成方法，而现有混合度量体系输出的不相关分数严重阻碍了该领域的发展。