Abstract

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.
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IEEE / Computer Vision Foundation (CVF)

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Rodriguez-Pardo, C., Casas, D., Garces, E., & Lopez-Moreno, J. (2024). TexTile: A Differentiable Metric for Texture Tileability. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4439-4449).

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