Research on AI-Driven Generation Mechanisms and Design Methodologies for Digital Media Art

Abstract

To address the problems of insufficient semantic control precision and lack of structured support for human-AI collaboration in digital media art generation, this study proposes a text–image cross-modal collaborative generation framework for artistic creation. Taking diffusion models and generative adversarial networks as generation kernels, the framework achieves high-precision mapping from text intent to visual features through cross-modal semantic alignment mechanisms, and realizes fine-grained control of the generation process by separating style, content, and structure variables via a conditional disentanglement module. A closed-loop human-AI collaboration process integrating intent modeling, constraint injection, and aesthetic evaluation is constructed, forming a reusable creative methodology system. Through multiple comparative experiments, the framework is quantitatively validated from generation quality, style diversity, and human-AI collaboration efficiency, demonstrating superior generation controllability and creative efficiency over single-model baseline methods. This study reveals the intrinsic mechanisms of AI involvement in digital media art creation, providing scientific methodological references for intelligent system design in this domain.