iPoster：基于图增强扩散模型的交互式海报布局生成
iPoster: Content-Aware Layout Generation for Interactive Poster Design via Graph-Enhanced Diffusion Models
AI总结：
iPoster通过图增强扩散模型实现交互式海报布局生成，支持用户指定约束条件，生成符合要求的布局，并在实验中表现出卓越的布局质量和可控性。
摘要：
我们提出了iPoster，一个交互式布局生成框架，使用户能够通过指定灵活的约束条件来指导内容感知的海报布局设计。iPoster允许用户在意图模块中指定部分意图，如元素类别、大小、位置或粗略的初始草案。然后，生成模块即时生成精细且上下文敏感的布局，这些布局忠实尊重这些约束。iPoster采用统一的图增强扩散架构，支持在用户指定的约束下执行各种设计任务。这些约束通过掩码策略在去噪步骤中精确保留用户输入。跨内容感知注意力模块将生成的元素与画布的显著区域对齐，确保视觉一致性。广泛的实验表明，iPoster不仅实现了最先进的布局质量，还提供了一个响应性强且可控的框架，用于具有约束条件的海报布局设计。
英文摘要：
We present iPoster, an interactive layout generation framework that empowers users to guide content-aware poster layout design by specifying flexible constraints. iPoster enables users to specify partial intentions within the intention module, such as element categories, sizes, positions, or coarse initial drafts. Then, the generation module instantly generates refined, context-sensitive layouts that faithfully respect these constraints. iPoster employs a unified graph-enhanced diffusion architecture that supports various design tasks under user-specified constraints. These constraints are enforced through masking strategies that precisely preserve user input at every denoising step. A cross content-aware attention module aligns generated elements with salient regions of the canvas, ensuring visual coherence. Extensive experiments show that iPoster not only achieves state-of-the-art layout quality, but offers a responsive and controllable framework for poster layout design with constraints.
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。
