TexTile: A Differentiable Metric for Texture Tileability

Carlos Rodriguez-Pardo, Dan Casas, Elena Garces, Jorge Lopez-Moreno; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 4439-4449

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.