Neural Cellular Automata (NCA) have proven to be effective in a variety of fields, with numerous biologically inspired applications. One of the fields, in which NCAs perform well is the generation of textures, modelling global patterns from local interactions governed by uniform and coherent rules. This paper aims to enhance the usability of NCAs in texture synthesis by addressing a shortcoming of current NCA architectures for texture generation, which requires separately trained NCA for each individual texture. In this work, we train a single NCA for the evolution of multiple textures, based on individual examples. Our solution provides texture information in the state of each cell, in the form of an internally coded genomic signal, which enables the NCA to generate the expected texture. Such a neural cellular automaton not only maintains its regenerative capability but also allows for interpolation between learned textures and supports grafting techniques. This demonstrates the ability to edit generated textures and the potential for them to merge and coexist within the same automaton. We also address questions related to the influence of the genomic information and the cost function on the evolution of the NCA.

翻译：神经细胞自动机（NCA）已在多个领域被证明是有效的，并催生了大量受生物学启发的应用。NCA表现卓越的领域之一便是纹理生成，它通过统一且一致的规则来调控局部相互作用，从而建模全局模式。本文旨在提升NCA在纹理合成中的可用性，针对当前用于纹理生成的NCA架构存在的一个不足——即每种纹理都需要单独训练一个NCA——提出改进。在本工作中，我们基于多个独立的示例纹理，训练了一个能够演化多种纹理的单一NCA。我们的解决方案在每个细胞的状态中以内部编码的基因组信号形式提供纹理信息，从而使NCA能够生成预期的纹理。这样的神经细胞自动机不仅保持了其再生能力，还允许在已学习纹理之间进行插值，并支持嫁接技术。这展示了编辑生成纹理的能力，以及在同一自动机中纹理融合与共存的潜力。我们还探讨了基因组信息和损失函数对NCA演化过程的影响相关问题。