PosterReward: Unlocking Accurate Evaluation for High-Quality Graphic Design Generation

Recent advancements in the text-rendering capabilities of image generation models have made the end-to-end creation of graphic design content, such as posters, increasingly feasible. However, existing reward models fail to accurately assess the quality of graphic design. They primarily focus on global image aesthetics and lack the capacity to evaluate two other core elements of graphic design: typography and layout. Furthermore, current text-to-image preference datasets suffer from a scarcity of data related to graphic design, which hinders the further development of generative models in this domain.To address this gap, we have designed an automated pipeline to construct a high-quality dataset of 70k poster preferences. Subsequently, we have developed, a reward model capable of accurately assessing the quality of generated posters, by leveraging a cascaded, multi-stage training pipeline. We also provide multiple variants of the model to cater to different application scenarios. Finally, we introduce and to evaluate the performance of existing reward models in poster assessment and the capabilities of current image generation models in poster creation, respectively.