Precipitation-Driven 3D Generative Modeling: A Framework for Reaction-Guided Spatial Occupancy, Local Mineral Deposition, and Long-Term Shape Evolution

Three-dimensional (3D) generative modeling has advanced through approaches such as 3D diffusion models, neural implicit representations, point-cloud generation, and Gaussian-based scene representations. However, many existing methods primarily formulate generation as the prediction, reconstruction, or refinement of geometric representations rather than as an explicit process in which material progressively occupies space through localized formation events. Inspired by precipitation and mineral deposition in natural environments, this paper proposes Precipitation-Driven 3D Generative Modeling (PD3GM), a conceptual framework that formulates 3D shape generation as a coupled process of chemical-state evolution, material formation, spatial deposition, and morphology-dependent growth. The framework represents the environment using a spatial field containing dissolved-material concentration, supersaturation, precipitation propensity, solid occupancy, and surface state. Local precipitation events convert available material into solid structures, while transport, surface attachment, geometric constraints, and optional dissolution govern subsequent evolution. A hybrid architecture combines process-based state updates with a learned proposal model, allowing the framework to generate shapes conditioned on target geometry, material properties, environmental conditions, or desired morphological characteristics. A voxel-based formulation, governing equations, training objectives, and a reference algorithm are presented. The framework is designed to support both physically motivated simulation and data-driven shape generation, including structures characterized by branching, layering, accumulation, and spatially heterogeneous growth. No experimental validation is claimed in this conceptual study. Its principal contribution is the proposed integration of precipitation-driven material formation with a general 3D generative architecture, together with a set of testable hypotheses and evaluation protocols for future implementation.