Research on Innovative Methods and Visual Efficacy of Artistic Graphic Design Based on Multimodal Generative AI
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Abstract
In the context of the deep integration of artificial intelligence generated content (AIGC) and digital creative industries, traditional art graphic design generally faces prominent problems such as long creative cycles, slow style iteration, visual expression homogenization, difficulty in multi-element collaboration, and difficulty in quantifying visual efficacy. Multimodal generative AI provides revolutionary technological support for the innovation of the entire process of artistic graphic design by leveraging the collaborative understanding and high-precision content generation capabilities of multisource information such as text, images, layout, color, and semantics. This article focuses on multimodal generative AI technology and systematically constructs an innovative method system for the entire process of artistic graphic design, which includes “requirement understanding multimodal guidance hierarchical generation precise control visual efficacy evaluation iterative optimization”; Deeply analyze multimodal collaborative generation mechanisms such as text guidance, image reference, sketch constraints, style transfer, layout alignment, etc; Propose innovative paradigms for poster design, brand vision, cultural and creative graphic design, packaging design, and other scenarios.
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