ReContraster: Making Your Posters Stand Out with Regional Contrast
Authors: Peixuan Zhang, Zijian Jia, Ziqi Cai, Shuchen Weng, Si Li, Boxin Shi
Organizations: School of Artificial Intelligence, Beijing University of Posts and Telecommunications · State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University · National Engineering Research Center of Visual Technology, School of Computer Science, Peking University · Beijing Academy of Artificial Intelligence
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.
Figures & tables
Figure 1: Illustration of our ReContraster for poster generation. Given a text description of the theme and visual texts, and a mask indicating region divisions, our model produces: (a) Visually striking poster designs with highly contrastive elements, compared to PosterGen. (b) Structurally balanced compositions with aesthetic harmony, compared to OpenCOLE. (c) Seamless and coherent transitions across region boundaries, compared to SD3.
Figure 2: Given a text description and a mask indicating region divisions, ReContraster initially uses an LLM Achiam et al. (2023) to extract the theme and visual texts separately. The extracted theme is then processed by the compositional multi-agent system in an iterative refinement loop. Within each iteration, the cognition agent identifies contrastive elements, the arranger agent organizes a layout in a JSON format, and a candidate image is generated based on this layout. This candidate is then evaluated by the refiner agent, which provides feedback to guide subsequent rounds of improvement until a design is approved. The generation of these candidate images relies on a pre-trained diffusion model equipped with the hybrid denoising strategy , where the gradient consistency loss and joint region denoising are applied to maintain content continuity and ensure harmonious transitions across region boundaries, respectively. The visual texts are finally rendered onto the generated image to produce a visually striking and aesthetically appealing poster.
Figure 3: Visual quality comparisons with text-to-image generation methods and poster generation methods.
Figure 4: Ablation study results with different variants of ReContraster.