Texture Regenerating and Grafting Using Genome-Driven Neural Cellular Automata

TL;DR

This paper introduces a novel neural cellular automata approach for multi-texture synthesis that enables self-regeneration of textures and seamless grafting of different textures during inference.

Contribution

The study presents a new training methodology for NCAs that supports texture self-healing and a grafting technique for combining textures without retraining.

Findings

Enables robust self-regeneration of textures in damaged regions.

Allows seamless texture grafting during inference phase.

Produces high-quality, complex textures with smooth transitions.

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.