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.

介绍了TexTile，一种新的可微度量标准，用于评估纹理图像能否被连接而不产生重复伪影（瓷砖性）。通过对纹理图像的内在可重复性进行明确分析，TexTile度量有效评估了纹理的可瓷砖性，为可瓷砖纹理的综合合成和分析提供了更准确的方法。同时，TexTile可与不同的最先进纹理合成方法结合使用，并在保持甚至提高总体纹理质量的同时产生可瓷砖纹理。此外，TexTile能客观评估任何可瓷砖纹理合成方法，而当前的混合度量标准产生了与之相关性不强的得分，严重阻碍了该领域的进展。

纹理可平铺性的可微度量