Concentric Layer Generation: An Adaptive Nested-Region Framework for Structured Image Synthesis

Abstract

Controllable image generation requires not only the synthesis of visually plausible content but also the organization of that content into coherent spatial structures. This paper proposes Concentric Layer Generation (CLG), a framework that organizes image synthesis around a shared center through a sequence of nested spatial regions. Unlike approaches that treat the image as a single generation domain or partition it into arbitrary regions, CLG explicitly represents the image as a hierarchy of concentric regions and generates their contents through conditional dependencies. The framework incorporates five components: concentric spatial decomposition, center-outward generation, adaptive layer-width allocation, inter-layer consistency modeling, and bidirectional refinement. The proposed formulation allows the generation process to allocate different spatial resolutions to different layers and revise previously generated regions when subsequent layers reveal structural or semantic inconsistencies. A unified objective combines image reconstruction, boundary compatibility, semantic consistency, and refinement stability. The framework can be implemented using pretrained diffusion models, masked image generation, or other conditional image synthesis architectures. This paper formalizes the method, presents an algorithmic specification, and develops an experimental protocol for evaluating its effectiveness against global generation, fixed-width concentric generation, and other region-based methods. No experimental results are claimed because the proposed framework has not yet been empirically validated. The central research question is whether explicit concentric organization, adaptive spatial allocation, and bidirectional inter-layer interaction can improve structural controllability and generation efficiency under suitable image-generation tasks.