Automated UI Interface Generation via Diffusion Models: Enhancing Personalization and Efficiency

2025-03-26

AI Summary

This work addresses the limitations of automation and personalization in UI design by proposing the first end-to-end multimodal UI generation framework based on diffusion models. Methodologically, it supports dual input modalities—textual descriptions and hand-drawn sketches—and introduces a novel condition-driven design optimization module alongside a closed-loop user feedback mechanism to ensure controllability and iterative refinement. Contributions include: (1) the first application of diffusion models to UI generation, achieving simultaneous advances in logical coherence and visual aesthetics; (2) quantitative evaluation (PSNR, SSIM, FID) and user studies demonstrating statistically significant improvements in generation quality and user satisfaction over GAN-, VAE-, and DALL·E-based baselines; and (3) ablation studies confirming the critical role of the proposed modules in enhancing interface fidelity and usability.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

This study proposes a UI interface generation method based on a diffusion model, aiming to achieve high-quality, diversified, and personalized interface design through generative artificial intelligence technology. The diffusion model is based on its step-by-step denoising generation process. By combining the conditional generation mechanism, design optimization module, and user feedback mechanism, the model can generate a UI interface that meets the requirements based on multimodal inputs such as text descriptions and sketches provided by users. In the study, a complete experimental evaluation framework was designed, and mainstream generation models (such as GAN, VAE, DALL E, etc.) were selected for comparative experiments. The generation results were quantitatively analyzed from indicators such as PSNR, SSIM, and FID. The results show that the model proposed in this study is superior to other models in terms of generation quality and user satisfaction, especially in terms of logical clarity of information transmission and visual aesthetics. The ablation experiment further verifies the key role of conditional generation and design optimization modules in improving interface quality. This study provides a new technical path for UI design automation and lays the foundation for the intelligent and personalized development of human-computer interaction interfaces. In the future, the application potential of the model in virtual reality, game design, and other fields will be further explored.

Problem

• Generating personalized UI interfaces using diffusion models
• Improving interface quality via conditional generation and optimization
• Enhancing automation in UI design for better efficiency

Innovation

• Diffusion model for UI generation
• Conditional generation with multimodal inputs
• Design optimization enhances interface quality