Neural Cellular Automata (NCAs) are bio-inspired dynamical systems in which identical cells iteratively apply a learned local update rule to self-organize into complex patterns, exhibiting regeneration, robustness, and spontaneous dynamics. Despite their success in texture synthesis and morphogenesis, NCAs remain largely confined to low-resolution outputs. This limitation stems from (1) training time and memory requirements that grow quadratically with grid size, (2) the strictly local propagation of information that impedes long-range cell communication, and (3) the heavy compute demands of real-time inference at high resolution. In this work, we overcome this limitation by pairing an NCA that evolves on a coarse grid with a lightweight implicit decoder that maps cell states and local coordinates to appearance attributes, enabling the same model to render outputs at arbitrary resolution. Moreover, because both the decoder and NCA updates are local, inference remains highly parallelizable. To supervise high-resolution outputs efficiently, we introduce task-specific losses for morphogenesis (growth from a seed) and texture synthesis with minimal additional memory and computation overhead. Our experiments across 2D/3D grids and mesh domains demonstrate that our hybrid models produce high-resolution outputs in real-time, and preserve the characteristic self-organizing behavior of NCAs.

翻译：神经细胞自动机（NCAs）是一种受生物启发的动态系统，其中相同细胞通过迭代应用经学习得到的局部更新规则，自组织形成复杂模式，展现出再生能力、鲁棒性和自发动态特性。尽管NCAs在纹理合成和形态发生领域取得显著成功，但其输出仍主要局限于低分辨率图像。这一限制源于：（1）训练时间和内存需求随网格尺寸呈二次增长；（2）严格局部信息传播机制阻碍了长程细胞通信；（3）高分辨率实时推理的沉重计算负担。为突破该限制，本研究提出在粗粒度网格上演化的NCA与轻量级隐式解码器相结合——该解码器将细胞状态与局部坐标映射为外观属性，使得同一模型能渲染任意分辨率的输出。由于解码器和NCA更新均具有局部性，推理过程仍保持高度可并行化。为高效监督高分辨率输出，我们针对形态发生（从种子生长）和纹理合成引入任务特异性损失函数，仅需极少的额外内存与计算开销。在二维/三维网格及网格域上的实验表明，该混合模型不仅能实时生成高分辨率输出，还保留了NCA特有的自组织行为特征。