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

TL;DR

This paper introduces a diffusion model-based framework for generating human-computer interfaces that enhances diversity, rationality, and personalization, surpassing traditional methods and demonstrating robustness across various conditions.

Contribution

It presents a novel diffusion-reverse diffusion process with conditional control for unified visual and interaction modeling in interface generation.

Findings

Outperforms existing models in error metrics

Maintains robustness under different conditions

Enhances diversity and personalization of generated interfaces

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.