ReContraster: Making Your Posters Stand Out with Regional Contrast

2026-04-11

AI Summary

This work proposes ReContraster, the first training-free method for poster generation that balances visual attention and clear communication through effective regional contrast. Inspired by the contrast effect in visual perception, ReContraster employs a multi-agent system to simulate designers’ cognitive processes—identifying visual elements, organizing layouts, and evaluating candidate designs. During the diffusion process, it introduces a hybrid denoising strategy to ensure smooth and harmonious transitions across region boundaries. Evaluated on a newly curated benchmark dataset, ReContraster significantly outperforms state-of-the-art methods across seven quantitative metrics and four user studies, consistently generating posters with greater visual impact and aesthetic coherence.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Effective poster design requires rapidly capturing attention and clearly conveying messages. Inspired by the ``contrast effects'' principle, we propose ReContraster, the first training-free model to leverage regional contrast to make posters stand out. By emulating the cognitive behaviors of a poster designer, ReContraster introduces the compositional multi-agent system to identify elements, organize layout, and evaluate generated poster candidates. To further ensure harmonious transitions across region boundaries, ReContraster integrates the hybrid denoising strategy during the diffusion process. We additionally contribute a new benchmark dataset for comprehensive evaluation. Seven quantitative metrics and four user studies confirm its superiority over relevant state-of-the-art methods, producing visually striking and aesthetically appealing posters.

Problem

• poster design
• regional contrast
• visual attention
• contrast effects
• aesthetic appeal

Innovation

• regional contrast
• training-free model
• multi-agent system
• diffusion process
• poster generation