π€ AI Summary
This work proposes a genome-driven neural cellular automata (NCA) framework to address the limitations of conventional multi-texture synthesis methods, which lack self-repair capabilities and flexible composition mechanisms. By initializing specific genomic channels during inference, the proposed approach enables autonomous regeneration of damaged regions and seamless grafting of heterogeneous textures without requiring retraining. The method transcends the constraints of static texture synthesis by supporting dynamic composition, high-quality generation of complex textures, and efficient self-repair of corrupted areas. Consequently, it significantly enhances the robustness and scalability of texture synthesis systems.
π Abstract
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.