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iPoster: Content-Aware Layout Generation for Interactive Poster Design via Graph-Enhanced Diffusion Models

Xudong Zhou, Jinyuan Liang, Qiuyi Guo, Guozheng Li

Abstract

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.

iPoster: Content-Aware Layout Generation for Interactive Poster Design via Graph-Enhanced Diffusion Models

Abstract

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.

Paper Structure

This paper contains 17 sections, 1 equation, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Content-aware Layout Generation
  4. Interactive Poster Design
  5. Method
  6. Preliminaries: User Constraints
  7. Content-Aware Layout Generation Model
  8. Model Inputs
  9. Constructing Graphs
  10. Cross Content-aware Attention Module
  11. Training and Inference Mechanisms
  12. Evaluation
  13. Datasets and Experiment Setup
  14. Evaluation Metrics
  15. Constrained Generation
  16. ...and 2 more sections

Figures (5)

  • Figure 1: The overall training framework of content-aware layout generation model (shown in the left). The core module is Cross Content-aware Attention Module (shown in the right).
  • Figure 2: The construction process of $G_{BLM}$ and $G_{ILM}$. For each $bbox_i$, $c$ is the element category, $cx$ and $cy$ are its center coordinates, and $w$ and $h$ are its width and height. Each image is divided into $r$ rows and $c$ columns of patches. See Section \ref{['sec:Constructing Graphs']} for details.
  • Figure 3: iPoster’s interactive framework, showing user interaction and mask execution during inference.
  • Figure 4: Test examples of different constrained generation tasks.
  • Figure 5: Illustration of a representative user scenario. The left side presents a schematic UI mockup depicting user interactions, and the right side shows the resulting layout candidates along with the final posters automatically rendered from them.