AI image generation technology for visual communication design
Abstract
This study addresses two key challenges in applying artificial intelligence (AI) image generation to visual communication design: insufficient alignment with design requirements and unstable style controllability. It systematically explores the integration of AI technology with professional design practice. First, the current research and application status of AI generation in design is reviewed, and its technological positioning is clarified. Next, an integrated framework is constructed by combining diffusion models, CLIP-based cross-modal alignment, and ControlNet spatial constraints. This framework supports an AI-driven design workflow that incorporates requirement analysis, controllable generation, and multi-solution exploration. Multi-dimensional evaluation metrics, including generation quality, peak signal-to-noise ratio, and requirement matching, are also established. Experiments are conducted across the core design scenarios-posters, brand logos, and mobile interfaces-using public datasets such as DesignBench-20 K and a custom commercial test set. The model performs best in mobile interface scenarios. Across all scenarios, the design cycle is reduced by over 91%. The model reliably generates outputs under textual requirement noise ≤ 10% and non-design data distribution shifts ≤ 40%. Ablation studies highlight the critical and differentiated contributions of each core component in layout control and requirement matching. The study further clarifies the application…
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.