Controllable GUI Exploration

CoRR（2025）

Cited 0|Views4

Abstract

During the early stages of interface design, designers need to produce multiple sketches to explore a design space. Design tools often fail to support this critical stage, because they insist on specifying more details than necessary. Although recent advances in generative AI have raised hopes of solving this issue, in practice they fail because expressing loose ideas in a prompt is impractical. In this paper, we propose a diffusion-based approach to the low-effort generation of interface sketches. It breaks new ground by allowing flexible control of the generation process via three types of inputs: A) prompts, B) wireframes, and C) visual flows. The designer can provide any combination of these as input at any level of detail, and will get a diverse gallery of low-fidelity solutions in response. The unique benefit is that large design spaces can be explored rapidly with very little effort in input-specification. We present qualitative results for various combinations of input specifications. Additionally, we demonstrate that our model aligns more accurately with these specifications than other models.

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【要点】：本文提出了一种基于扩散方法的新型界面设计草图生成方法，可以通过提示、线框和视觉流三种输入方式实现灵活控制，有效支持设计师在早期设计阶段对设计空间的探索。

【方法】：研究采用了一种基于扩散的生成模型，允许设计师以不同详细程度的输入（提示、线框、视觉流）来控制界面设计草图的生成过程。

【实验】：研究展示了多种输入规范组合的定性结果，并通过实验证明了与其他模型相比，所提出的模型更能准确地对输入规范进行对齐。具体使用的数据集名称在论文中未提及。

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