Reconfiguration-Based 3D Generation: A Constraint-Aware Framework for Generating Three-Dimensional Shapes Through the Rearrangement of Existing Components
Abstract
Most three-dimensional (3D) generative methods construct object geometry by sampling latent representations, denoising noisy structures, predicting spatial primitives, or optimizing implicit and explicit geometric fields. However, many natural and engineered objects emerge through the reorganization of existing components rather than the unrestricted creation of new geometric elements. Examples include granular rearrangement, modular assembly, folding, and molecular self-assembly. This paper proposes Reconfiguration-Based 3D Generation (R3G), a framework that formulates 3D shape generation as the constrained reorganization of an existing collection of geometric components. Given an initial component set and a target specification, R3G jointly optimizes component transformations, connection topology, and selective local deformation while explicitly controlling component preservation, geometric compatibility, structural stability, and reconstruction fidelity. The proposed framework represents an object as a graph of geometric components, with rigid transformations describing spatial rearrangement and discrete variables describing connection changes. A constraint-aware optimization objective combines target-shape agreement, component preservation, collision avoidance, connection validity, and physical feasibility. The framework also introduces a preservation-aware generation regime in which the model can select among rigid rearrangement, topology modification, and limited deformation according to the target requirements. Unlike a method that assumes every target must be constructed from scratch, R3G treats the initial components as reusable generative resources. This paper specifies the mathematical formulation, model architecture, training procedure, evaluation protocol, and anticipated limitations of the approach. Since the proposed method has not yet been experimentally implemented or validated, no empirical performance improvements are claimed. The contribution is a research framework for investigating whether explicit component preservation and constraint-aware reconfiguration can improve controllability, structural consistency, and reuse in 3D generation.
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BibTeX
@misc{zhang2026reconfigurationbased,
title = {{Reconfiguration-Based 3D Generation: A Constraint-Aware Framework for Generating Three-Dimensional Shapes Through the Rearrangement of Existing Components}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23636400},
url = {https://zenodo.org/records/23636400},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Reconfiguration-Based 3D Generation: A Constraint-Aware Framework for Generating Three-Dimensional Shapes Through the Rearrangement of Existing Components
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/25
PB - Zenodo
DO - 10.5281/zenodo.23636400
UR - https://zenodo.org/records/23636400
AB - Most three-dimensional (3D) generative methods construct object geometry by sampling latent representations, denoising noisy structures, predicting spatial primitives, or optimizing implicit and explicit geometric fields. However, many natural and engineered objects emerge through the reorganization of existing components rather than the unrestricted creation of new geometric elements. Examples include granular rearrangement, modular assembly, folding, and molecular self-assembly. This paper proposes Reconfiguration-Based 3D Generation (R3G), a framework that formulates 3D shape generation as the constrained reorganization of an existing collection of geometric components. Given an initial component set and a target specification, R3G jointly optimizes component transformations, connection topology, and selective local deformation while explicitly controlling component preservation, geometric compatibility, structural stability, and reconstruction fidelity. The proposed framework represents an object as a graph of geometric components, with rigid transformations describing spatial rearrangement and discrete variables describing connection changes. A constraint-aware optimization objective combines target-shape agreement, component preservation, collision avoidance, connection validity, and physical feasibility. The framework also introduces a preservation-aware generation regime in which the model can select among rigid rearrangement, topology modification, and limited deformation according to the target requirements. Unlike a method that assumes every target must be constructed from scratch, R3G treats the initial components as reusable generative resources. This paper specifies the mathematical formulation, model architecture, training procedure, evaluation protocol, and anticipated limitations of the approach. Since the proposed method has not yet been experimentally implemented or validated, no empirical performance improvements are claimed. The contribution is a research framework for investigating whether explicit component preservation and constraint-aware reconfiguration can improve controllability, structural consistency, and reuse in 3D generation.
ER -