Bubble-Assembly 3D Generation: A Generative Framework Based on Bubble Formation, Spatial Competition, and Foam Aggregation
Abstract
Existing three-dimensional (3D) generative methods commonly construct objects through learned geometric representations, latent-variable transformations, point-cloud synthesis, or continuous volumetric rendering. This paper proposes Bubble-Assembly 3D Generation (BA3D), a conceptual framework that investigates an alternative generative mechanism inspired by bubble formation and foam aggregation. Instead of treating bubbles merely as visual elements or using them exclusively to reproduce fluid phenomena, BA3D treats a population of interacting bubble entities as a computational substrate for constructing target 3D structures.
Each bubble is represented by a state containing its spatial position, effective size, shape, pressure, interface properties, and lifecycle status. The population evolves through nucleation, growth, displacement, deformation, contact, coalescence, rupture, dissolution, and elimination. These processes collectively determine the evolving geometry and topology of the generated structure. Spatial competition restricts the configurations that can simultaneously occupy a region, while interface constraints regulate contact and separation. A target-conditioned controller influences bubble formation and interaction so that the emergent structure approaches a desired 3D shape.
The proposed framework combines a variable-cardinality bubble representation, interaction-graph dynamics, implicit geometric reconstruction, and a composite optimization objective. Unlike approaches that require a fixed number of geometric primitives throughout generation, BA3D explicitly permits the creation, merging, splitting, and removal of entities. This makes changes in the number and connectivity of the underlying entities part of the generative process itself.
The paper formalizes the framework, presents a possible implementation algorithm, defines training and evaluation objectives, and proposes experiments comparing BA3D with established 3D generation baselines. Existing research already addresses physically based bubble and foam simulation, including interface tracking and topological changes. Accordingly, the principal contribution claimed here is the formulation of a target-conditioned 3D generation framework organized around bubble-population lifecycle dynamics, rather than a claim that bubble simulation or foam-based geometric reconstruction is entirely new. Empirical performance and the extent of its advantages remain to be established experimentally.
Related
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- Dynamic Sentence-Position Competition for Text Generation: A Framework for Candidate Competition, Joint Ordering, and Global Coherence Optimization
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BibTeX
@misc{zhang2026bubbleassembly,
title = {{Bubble-Assembly 3D Generation: A Generative Framework Based on Bubble Formation, Spatial Competition, and Foam Aggregation}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23635968},
url = {https://zenodo.org/records/23635968},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Bubble-Assembly 3D Generation: A Generative Framework Based on Bubble Formation, Spatial Competition, and Foam Aggregation
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/25
PB - Zenodo
DO - 10.5281/zenodo.23635968
UR - https://zenodo.org/records/23635968
AB - Existing three-dimensional (3D) generative methods commonly construct objects through learned geometric representations, latent-variable transformations, point-cloud synthesis, or continuous volumetric rendering. This paper proposes Bubble-Assembly 3D Generation (BA3D), a conceptual framework that investigates an alternative generative mechanism inspired by bubble formation and foam aggregation. Instead of treating bubbles merely as visual elements or using them exclusively to reproduce fluid phenomena, BA3D treats a population of interacting bubble entities as a computational substrate for constructing target 3D structures. Each bubble is represented by a state containing its spatial position, effective size, shape, pressure, interface properties, and lifecycle status. The population evolves through nucleation, growth, displacement, deformation, contact, coalescence, rupture, dissolution, and elimination. These processes collectively determine the evolving geometry and topology of the generated structure. Spatial competition restricts the configurations that can simultaneously occupy a region, while interface constraints regulate contact and separation. A target-conditioned controller influences bubble formation and interaction so that the emergent structure approaches a desired 3D shape. The proposed framework combines a variable-cardinality bubble representation, interaction-graph dynamics, implicit geometric reconstruction, and a composite optimization objective. Unlike approaches that require a fixed number of geometric primitives throughout generation, BA3D explicitly permits the creation, merging, splitting, and removal of entities. This makes changes in the number and connectivity of the underlying entities part of the generative process itself. The paper formalizes the framework, presents a possible implementation algorithm, defines training and evaluation objectives, and proposes experiments comparing BA3D with established 3D generation baselines. Existing research already addresses physically based bubble and foam simulation, including interface tracking and topological changes. Accordingly, the principal contribution claimed here is the formulation of a target-conditioned 3D generation framework organized around bubble-population lifecycle dynamics, rather than a claim that bubble simulation or foam-based geometric reconstruction is entirely new. Empirical performance and the extent of its advantages remain to be established experimentally.
ER -