This study significantly advances multi-texture synthesis using Neural Cellular Automata (NCAs) by introducing a novel training methodology that enables robust self-regeneration of textures in damaged regions. This inherent healing mechanism, essential for dynamic and adaptive systems, extends beyond traditional computer graphics applications, highlighting the fundamental self-organizing properties of NCAs. Furthermore, we present a versatile grafting technique, enabling the seamless combination of distinct textures. This is achieved efficiently during the inference phase, without requiring specialized retraining, through precise initialization of the NCA's genome channels. Our findings demonstrate the generation of high-quality, complex textures with fluid transitions, showcasing a powerful and efficient paradigm for dynamic texture composition and self-repair in autonomous systems.

翻译：本研究通过引入一种新颖的训练方法，显著推进了基于神经细胞自动机（NCA）的多纹理合成技术，使其能够在受损区域实现纹理的鲁棒性自我再生。这种固有的修复机制对动态自适应系统至关重要，不仅超越了传统计算机图形学应用范畴，更凸显了NCA的基本自组织特性。此外，我们提出了一种通用的嫁接技术，通过精确初始化NCA的基因组通道，在推理阶段即可高效实现不同纹理的无缝组合，无需专门的重新训练。实验结果表明，该方法能够生成具有流畅过渡的高质量复杂纹理，为动态纹理组合与自主系统的自我修复提供了一种强大且高效的范式。