用视觉流控制生成界面草图，轻松探索设计空间
ControlGUI: Guiding Generative GUI Exploration through Perceptual Visual Flow
- 通过提示、线稿和视觉流三类输入灵活控制生成过程
- 支持低细节输入，快速产出多样化的低保真设计方案
- 适合交互设计初学者或快速原型阶段的设计师使用
在界面设计初期，设计师需生成多个草图以探索设计空间，但现有设计工具往往要求过多细节，难以支持此阶段。尽管生成式AI带来希望，实际中通过提示表达模糊想法仍不实用。本文提出一种基于扩散模型的方法，实现低门槛的界面草图生成。该方法允许用户以提示、线稿或视觉流任意组合作为输入，且可在任意细节层级操作，响应生成多样化的低保真设计方案。其独特优势在于能以极低输入成本快速探索大范围设计空间。我们展示了多种输入组合的定性结果，并验证本模型对输入规范的对齐度优于其他模型。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。