发表机构
Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ReVision是一款AI工具，通过将视觉与文本参考分解为可编辑的概念解读和视觉母题，并支持跨空间重组与发散变体生成，帮助设计师（尤其是新手）拓展设计空间，避免固着于参考。
AI 中文摘要
视觉设计师从参考资料中获取灵感，以拓展其设计空间。他们将参考资料的感染力分解为概念性元素和视觉元素，范围从对象和颜色等显性属性，到较难言明的概念和视觉母题。随后，他们创作不同的视觉形式，以探索所选元素如何以不同方式组合。新手常常在这些步骤上遇到困难，反而专注于表面特征或产生有限的视觉变化，从而固着于参考资料。现有工具支持可编辑的视觉属性和高层次主题，但对概念性解读如何与表现性视觉母题相关联，或其组合如何被系统地重新表达，提供的控制有限。我们提出ReVision，一种基于AI的工具，将视觉和文本参考资料分解为可编辑的概念性解读和视觉母题，支持它们在概念空间和视觉空间中的重组，并将每个方向渲染为发散的视觉形式变体，从而在创作过程中支持更发散的探索。
英文摘要
Visual designers get inspiration from references to expand their design space. They decompose what makes a reference evocative into conceptual and visual elements, ranging from explicit attributes such as objects and colors, to less readily articulated concepts and visual motifs. They then create different visual forms to explore how the selected elements could be combined differently. Novices often struggle with these moves, instead focusing on surface features or producing limited visual variation, thus becoming fixated on the reference. Existing tools support editable visual attributes and high-level themes, but provide limited control over how conceptual interpretations relate to expressive visual motifs or how their combinations can be systematically re-expressed. We present ReVision, an AI-based tool that decomposes visual and textual references into editable conceptual interpretations and visual motifs, enables their recombination across conceptual and visual spaces, and renders each direction as divergent visual-form variations, supporting more divergent exploration during the creation process.
CommentsUIST'26 Poster
