Surface-Tension-Driven 3D Generative Modeling: A Framework for Energy-Minimizing Interface Competition and Dynamic Geometric Formation
Abstract
Three-dimensional generative models have achieved substantial progress through diffusion models, implicit neural representations, point-cloud generation, and Gaussian-based scene representations. However, these approaches generally construct geometry through learned distributions, explicit geometric operations, or combinations of learned generation and physical optimization. This paper proposes Surface-Tension-Driven 3D Generative Modeling (ST3D-GM), a conceptual framework in which surface-tension-inspired energy minimization becomes a central mechanism for generating three-dimensional shapes. Rather than treating surface tension exclusively as a physical property to be simulated or a constraint applied after generation, ST3D-GM uses evolving interfaces as computational entities that compete, deform, merge, separate, and reorganize under an objective combining interfacial energy, geometric constraints, and target-shape guidance. The framework represents candidate shapes using a differentiable three-dimensional interface field and iteratively updates the field to produce geometries that balance surface regularity, volume preservation, interface interactions, and target compatibility. A multi-interface extension introduces competition between candidate geometric components, while a generative prior can preserve semantic plausibility during energy-driven evolution. We formulate the energy functional, describe the optimization procedure, present a modular algorithm, and propose an experimental protocol for evaluating geometric quality, target fidelity, energy reduction, and computational efficiency. The proposed framework is a research hypothesis rather than a validated generative model; its novelty and effectiveness require systematic comparison with existing physical simulation and physics-guided 3D generation methods.
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BibTeX
@misc{zhang2026surfacetensiondriven,
title = {{Surface-Tension-Driven 3D Generative Modeling: A Framework for Energy-Minimizing Interface Competition and Dynamic Geometric Formation}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23635978},
url = {https://zenodo.org/records/23635978},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Surface-Tension-Driven 3D Generative Modeling: A Framework for Energy-Minimizing Interface Competition and Dynamic Geometric Formation
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/25
PB - Zenodo
DO - 10.5281/zenodo.23635978
UR - https://zenodo.org/records/23635978
AB - Three-dimensional generative models have achieved substantial progress through diffusion models, implicit neural representations, point-cloud generation, and Gaussian-based scene representations. However, these approaches generally construct geometry through learned distributions, explicit geometric operations, or combinations of learned generation and physical optimization. This paper proposes Surface-Tension-Driven 3D Generative Modeling (ST3D-GM), a conceptual framework in which surface-tension-inspired energy minimization becomes a central mechanism for generating three-dimensional shapes. Rather than treating surface tension exclusively as a physical property to be simulated or a constraint applied after generation, ST3D-GM uses evolving interfaces as computational entities that compete, deform, merge, separate, and reorganize under an objective combining interfacial energy, geometric constraints, and target-shape guidance. The framework represents candidate shapes using a differentiable three-dimensional interface field and iteratively updates the field to produce geometries that balance surface regularity, volume preservation, interface interactions, and target compatibility. A multi-interface extension introduces competition between candidate geometric components, while a generative prior can preserve semantic plausibility during energy-driven evolution. We formulate the energy functional, describe the optimization procedure, present a modular algorithm, and propose an experimental protocol for evaluating geometric quality, target fidelity, energy reduction, and computational efficiency. The proposed framework is a research hypothesis rather than a validated generative model; its novelty and effectiveness require systematic comparison with existing physical simulation and physics-guided 3D generation methods.
ER -