DreamPoster: A Unified Framework for Image-Conditioned Generative Poster
  Design
Authors: name: Xiwei Hu, name: Haokun Chen, name: Zhongqi Qi, name: Hui Zhang, name: Dexiang Hong, name: Jie Shao, name: Xinglong Wu

We present DreamPoster, a Text-to-Image generation framework that
intelligently synthesizes high-quality posters from user-provided images and
text prompts while maintaining content fidelity and supporting flexible
resolution and layout outputs. Specifically, DreamPoster is built upon our T2I
model, Seedream3.0 to uniformly process different poster generating types. For
dataset construction, we propose a systematic data annotation pipeline that
precisely annotates textual content and typographic hierarchy information
within poster images, while employing comprehensive methodologies to construct
paired datasets comprising source materials (e.g., raw graphics/text) and their
corresponding final poster outputs. Additionally, we implement a progressive
training strategy that enables the model to hierarchically acquire multi-task
generation capabilities while maintaining high-quality generation. Evaluations
on our testing benchmarks demonstrate DreamPoster's superiority over existing
methods, achieving a high usability rate of 88.55\%, compared to GPT-4o
(47.56\%) and SeedEdit3.0 (25.96\%). DreamPoster will be online in Jimeng and
other Bytedance Apps.