Stained-Glass Generation: A Fragment-Based Framework for Structured Image Synthesis Through Adaptive Partitioning, Color Coordination, and Dynamic Glass-Cell Evolution
Abstract
Contemporary image-generation systems commonly represent images through pixels, latent variables, neural feature maps, or learned distributions. An alternative approach is to construct images from explicitly represented visual components whose geometry, color, adjacency, and collective organization determine the final result. This paper introduces Stained-Glass Generation (SGG), a fragment-based framework for structured image synthesis in which an image is represented as an evolving collection of colored glass-like cells separated by structural boundaries. Rather than treating stained glass exclusively as a post-processing effect, SGG treats its constituent fragments as primary units of image representation and generation. Each fragment is associated with geometric, chromatic, material, and topological attributes. The framework supports initial partitioning, fragment placement, color assignment, boundary construction, adaptive splitting and merging, and iterative image-level optimization. A rendering function combines the fragment representation with optional transparency, illumination, and surface effects to produce the final image. The proposed objective balances image fidelity, perceptual structure, boundary quality, color coherence, geometric validity, and representational complexity. Two operating modes are distinguished: conditional reconstruction from a reference image and unconditional or text-conditioned image synthesis. A modular optimization strategy is described, with reinforcement learning presented as an optional mechanism for selecting discrete structural actions. The paper also defines a reproducible experimental protocol for evaluating reconstruction quality, perceptual quality, structural organization, computational cost, and the contribution of individual components. SGG is presented as a research framework rather than an empirically validated replacement for existing image-generation methods. Its principal research question is whether explicitly evolving image fragments can provide a controllable and interpretable alternative representation for generating structured visual content.
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BibTeX
@misc{zhang2026stainedglass,
title = {{Stained-Glass Generation: A Fragment-Based Framework for Structured Image Synthesis Through Adaptive Partitioning, Color Coordination, and Dynamic Glass-Cell Evolution}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23555866},
url = {https://zenodo.org/records/23555866},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Stained-Glass Generation: A Fragment-Based Framework for Structured Image Synthesis Through Adaptive Partitioning, Color Coordination, and Dynamic Glass-Cell Evolution
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/22
PB - Zenodo
DO - 10.5281/zenodo.23555866
UR - https://zenodo.org/records/23555866
AB - Contemporary image-generation systems commonly represent images through pixels, latent variables, neural feature maps, or learned distributions. An alternative approach is to construct images from explicitly represented visual components whose geometry, color, adjacency, and collective organization determine the final result. This paper introduces Stained-Glass Generation (SGG), a fragment-based framework for structured image synthesis in which an image is represented as an evolving collection of colored glass-like cells separated by structural boundaries. Rather than treating stained glass exclusively as a post-processing effect, SGG treats its constituent fragments as primary units of image representation and generation. Each fragment is associated with geometric, chromatic, material, and topological attributes. The framework supports initial partitioning, fragment placement, color assignment, boundary construction, adaptive splitting and merging, and iterative image-level optimization. A rendering function combines the fragment representation with optional transparency, illumination, and surface effects to produce the final image. The proposed objective balances image fidelity, perceptual structure, boundary quality, color coherence, geometric validity, and representational complexity. Two operating modes are distinguished: conditional reconstruction from a reference image and unconditional or text-conditioned image synthesis. A modular optimization strategy is described, with reinforcement learning presented as an optional mechanism for selecting discrete structural actions. The paper also defines a reproducible experimental protocol for evaluating reconstruction quality, perceptual quality, structural organization, computational cost, and the contribution of individual components. SGG is presented as a research framework rather than an empirically validated replacement for existing image-generation methods. Its principal research question is whether explicitly evolving image fragments can provide a controllable and interpretable alternative representation for generating structured visual content.
ER -