Texture Regenerating and Grafting Using Genome-Driven Neural Cellular Automata
For computer graphics and autonomous systems, it provides a dynamic texture synthesis approach with self-repair and composition capabilities, though it is an incremental improvement over existing NCA methods.
This work introduces a training method for Neural Cellular Automata that enables textures to self-heal damaged regions and a grafting technique to combine textures without retraining, achieving high-quality, fluid transitions.
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.