ReContraster: Making Your Posters Stand Out with Regional Contrast
For graphic designers and automated poster generation, ReContraster provides a novel approach to enhance visual appeal without training, though the problem is domain-specific and incremental.
ReContraster introduces a training-free model that uses regional contrast to make posters stand out, outperforming state-of-the-art methods across seven quantitative metrics and four user studies.
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.