ReLayout: 通过关系感知的设计重建实现多功能且结构保持的设计布局编辑
ReLayout: Versatile and Structure-Preserving Design Layout Editing via Relation-Aware Design Reconstruction
AI总结：
ReLayout通过关系感知设计重建实现多功能且结构保持的设计布局编辑，无需三元组数据，提升编辑质量和结构保持性。
摘要：
自动化重设计无需手动调整标志着设计流程中的关键进步。在本工作中，我们专注于一个基础的重设计任务，称为设计布局编辑，其目的是根据用户意图自动修改设计的几何组成。为了解决自然语言中用户需求的模糊性，我们引入了四种基本且重要的编辑操作，并规范了编辑操作的格式。未探索的任务带来了独特的挑战：在满足指定的编辑操作的同时，同时保持未编辑元素的布局结构。此外，三元组（原始设计、编辑操作、编辑后设计）样本的稀缺性也构成了另一个严峻的挑战。为此，我们提出了ReLayout，一种新的框架，用于多功能且结构保持的设计布局编辑，无需三元组数据。具体而言，ReLayout首先引入了关系图，其中包含未编辑元素之间的位置和大小关系，作为布局结构保持的约束。然后提出了关系感知的设计重建（RADR），以绕过数据挑战。通过学习从其元素、关系图和合成的编辑操作中重建设计，RADR有效地以自监督的方式模拟编辑过程。多模态大语言模型作为RADR的骨干，统一了多种编辑操作在一个模型中，从而在微调后实现多功能编辑。定性、定量结果和用户研究显示，ReLayout在编辑质量、准确性和布局结构保持方面显著优于基线模型。
英文摘要：
Automated redesign without manual adjustments marks a key step forward in the design workflow. In this work, we focus on a foundational redesign task termed design layout editing, which seeks to autonomously modify the geometric composition of a design based on user intents. To overcome the ambiguity of user needs expressed in natural language, we introduce four basic and important editing actions and standardize the format of editing operations. The underexplored task presents a unique challenge: satisfying specified editing operations while simultaneously preserving the layout structure of unedited elements. Besides, the scarcity of triplet (original design, editing operation, edited design) samples poses another formidable challenge. To this end, we present ReLayout, a novel framework for versatile and structure-preserving design layout editing that operates without triplet data. Specifically, ReLayout first introduces the relation graph, which contains the position and size relationships among unedited elements, as the constraint for layout structure preservation. Then, relation-aware design reconstruction (RADR) is proposed to bypass the data challenge. By learning to reconstruct a design from its elements, a relation graph, and a synthesized editing operation, RADR effectively emulates the editing process in a self-supervised manner. A multi-modal large language model serves as the backbone for RADR, unifying multiple editing actions within a single model and thus achieving versatile editing after fine-tuning. Qualitative, quantitative results and user studies show that ReLayout significantly outperforms the baseline models in terms of editing quality, accuracy, and layout structure preservation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
