Crystallization-Based Image Generation: A Framework for Multi-Nucleation, Competitive Growth, and Reward-Guided Visual Synthesis
Abstract
Contemporary image generation methods commonly construct images through learned transformations of latent representations, iterative denoising, autoregressive prediction, or other parameterized generative procedures. This paper proposes Crystallization-Based Image Generation (CBIG), a conceptual framework that models image construction as a dynamic process of nucleation, local attachment, competitive growth, facet formation, secondary nucleation, dissolution, and restructuring. Instead of treating image generation exclusively as the direct prediction of pixel values or the refinement of a global latent representation, CBIG explicitly maintains a spatial field containing multiple generative nuclei and a set of local growth states. Each nucleus proposes candidate visual elements, evaluates their compatibility with neighboring structures and global generation objectives, and competes with other nuclei for spatial regions. A controller determines whether to expand, split, merge, suppress, dissolve, or reactivate a nucleus. The framework can be combined with learned visual representations, conditional generation models, and reinforcement learning to optimize growth decisions under quality and computational constraints. We formalize the image state, attachment dynamics, competitive ownership, adaptive nucleation, restructuring operations, reward functions, and stopping criteria. We further present a reference algorithm and an experimental protocol for evaluating the framework against single-seed growth, multi-seed growth, cellular-automaton-based generation, and appropriate learned image-generation baselines. The proposed framework is a research hypothesis rather than a validated generative model. Its central contribution is the explicit organization of image synthesis around interacting, dynamically managed growth centers. Whether this organization improves visual quality, controllability, computational efficiency, or structural coherence remains an empirical question.
Related
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BibTeX
@misc{zhang2026crystallizationbased,
title = {{Crystallization-Based Image Generation: A Framework for Multi-Nucleation, Competitive Growth, and Reward-Guided Visual Synthesis}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23555895},
url = {https://zenodo.org/records/23555895},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Crystallization-Based Image Generation: A Framework for Multi-Nucleation, Competitive Growth, and Reward-Guided Visual Synthesis
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/22
PB - Zenodo
DO - 10.5281/zenodo.23555895
UR - https://zenodo.org/records/23555895
AB - Contemporary image generation methods commonly construct images through learned transformations of latent representations, iterative denoising, autoregressive prediction, or other parameterized generative procedures. This paper proposes Crystallization-Based Image Generation (CBIG), a conceptual framework that models image construction as a dynamic process of nucleation, local attachment, competitive growth, facet formation, secondary nucleation, dissolution, and restructuring. Instead of treating image generation exclusively as the direct prediction of pixel values or the refinement of a global latent representation, CBIG explicitly maintains a spatial field containing multiple generative nuclei and a set of local growth states. Each nucleus proposes candidate visual elements, evaluates their compatibility with neighboring structures and global generation objectives, and competes with other nuclei for spatial regions. A controller determines whether to expand, split, merge, suppress, dissolve, or reactivate a nucleus. The framework can be combined with learned visual representations, conditional generation models, and reinforcement learning to optimize growth decisions under quality and computational constraints. We formalize the image state, attachment dynamics, competitive ownership, adaptive nucleation, restructuring operations, reward functions, and stopping criteria. We further present a reference algorithm and an experimental protocol for evaluating the framework against single-seed growth, multi-seed growth, cellular-automaton-based generation, and appropriate learned image-generation baselines. The proposed framework is a research hypothesis rather than a validated generative model. Its central contribution is the explicit organization of image synthesis around interacting, dynamically managed growth centers. Whether this organization improves visual quality, controllability, computational efficiency, or structural coherence remains an empirical question.
ER -