Field-Driven Image Generation: A Framework for Dynamic Field-Mediated Visual Synthesis

Abstract

Contemporary image generation methods commonly construct images through learned latent transformations, iterative denoising, autoregressive prediction, or explicit rendering procedures. This paper proposes Field-Driven Image Generation (FDIG), a conceptual framework in which visual synthesis is organized around the evolution of spatially distributed fields that influence image formation. Rather than treating a field solely as a rendering representation or as a vector field that transports a probability distribution, FDIG considers a collection of potentially heterogeneous fields that jointly regulate visual-element placement, appearance, structural organization, and local refinement. The proposed framework includes scalar, vector, semantic, appearance, uncertainty, and interaction fields. These fields influence a visual state through a field-mediated update operator and may themselves change in response to the evolving image, task conditions, and evaluation feedback. The formulation accommodates fixed-field generation, coupled-field generation, and adaptive field reconfiguration as progressively more expressive configurations. A unified objective is proposed to balance target consistency, structural coherence, diversity, computational cost, and field-update complexity. The paper also presents an implementable algorithm, a staged experimental protocol, ablation studies, evaluation criteria, and limitations. The framework is positioned in relation to existing work on diffusion models, flow matching, neural fields, and neural cellular automata. Its potential contribution is the explicit organization of heterogeneous, dynamically coupled fields as a general control layer for visual synthesis, rather than the introduction of spatial fields themselves. Since no empirical experiments are reported in this manuscript, the effectiveness, scalability, and distinctiveness of the proposed framework remain hypotheses requiring systematic validation.