Neural Cellular Automata (NCA) have shown a remarkable ability to learn the
required rules to "grow" images, classify morphologies, segment images, as well
as to do general computation such as path-finding. We believe the inductive
prior they introduce lends itself to the generation of textures. Textures in
the natural world are often generated by variants of locally interacting
reaction-diffusion systems. Human-made textures are likewise often generated in
a local manner (textile weaving, for instance) or using rules with local
dependencies (regular grids or geometric patterns). We demonstrate learning a
texture generator from a single template image, with the generation method
being embarrassingly parallel, exhibiting quick convergence and high fidelity
of output, and requiring only some minimal assumptions around the underlying
state manifold. Furthermore, we investigate properties of the learned models
that are both useful and interesting, such as non-stationary dynamics and an
inherent robustness to damage. Finally, we make qualitative claims that the
behaviour exhibited by the NCA model is a learned, distributed, local algorithm
to generate a texture, setting our method apart from existing work on texture
generation. We discuss the advantages of such a paradigm.

本文利用神经元细胞自动机（NCA）算法实现了基于单个模板图像的纹理生成器，其中神经元细胞自动机算法提供了一种学习分布式本地算法生成纹理的范例，并具有高保真度和鲁棒性。