Generative Modeling of Human-Computer Interfaces with Diffusion Processes and Conditional Control

2026-01-11

arXiv.org

AI Summary

This work addresses the limitations of traditional template- or rule-based human-computer interface generation methods, which struggle to accommodate interface diversity, layout complexity, and personalized user requirements. The authors propose a novel generative framework based on a diffusion–reverse diffusion process, introducing for the first time a conditional control mechanism during the reverse diffusion phase. This mechanism integrates user intent, contextual state, and task constraints to jointly model both visual appearance and interactive logic of interfaces. Regularization constraints and multi-objective optimization are further incorporated to ensure the plausibility and stability of generated outputs. Experimental results on public datasets demonstrate that the proposed method outperforms existing approaches across multiple metrics—including MSE, SSIM, PSNR, and MAE—and exhibits strong robustness under varying parameter settings and environmental conditions.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This study investigates human-computer interface generation based on diffusion models to overcome the limitations of traditional template-based design and fixed rule-driven methods. It first analyzes the key challenges of interface generation, including the diversity of interface elements, the complexity of layout logic, and the personalization of user needs. A generative framework centered on the diffusion-reverse diffusion process is then proposed, with conditional control introduced in the reverse diffusion stage to integrate user intent, contextual states, and task constraints, enabling unified modeling of visual presentation and interaction logic. In addition, regularization constraints and optimization objectives are combined to ensure the rationality and stability of the generated interfaces. Experiments are conducted on a public interface dataset with systematic evaluations, including comparative experiments, hyperparameter sensitivity tests, environmental sensitivity tests, and data sensitivity tests. Results show that the proposed method outperforms representative models in mean squared error, structural similarity, peak signal-to-noise ratio, and mean absolute error, while maintaining strong robustness under different parameter settings and environmental conditions. Overall, the diffusion model framework effectively improves the diversity, rationality, and intelligence of interface generation, providing a feasible solution for automated interface generation in complex interaction scenarios.

Problem

• human-computer interface
• generative modeling
• diffusion processes
• conditional control
• interface generation

Innovation

• diffusion models
• conditional control
• interface generation
• human-computer interaction
• generative modeling