  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.
