DreamPoster: A Unified Framework for Image-Conditioned Generative Poster
  Design
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.
68763aea257d4f04353706a6
Xiwei Hu
claimed_verified
2025-07-15T19:08:01.066Z
6436766d0b2c0d86d0b11d68
/avatars/1157cdaf47c313d1e879049a987ed0a6.svg
Melodie Hu
user
melohux
68763aea257d4f04353706a7
Haokun Chen
68763aea257d4f04353706a8
Zhongqi Qi
claimed_verified
2025-07-15T19:07:59.018Z
63e3b7bec65f975b436d12be
https://cdn-avatars.huggingface.co/v1/production/uploads/63e3b7bec65f975b436d12be/guGZu6TBQJMBcrbaigjag.jpeg
Qi
user
GeorgeQi
68763aea257d4f04353706a9
Hui Zhang
68763aea257d4f04353706aa
Dexiang Hong
68763aea257d4f04353706ab
Jie Shao
68763aea257d4f04353706ac
Xinglong Wu
DreamPoster: A Unified Framework for Image-Conditioned Generative Poster
  Design
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.
DreamPoster, a Text-to-Image framework using Seedream3.0, generates high-quality posters with flexible resolution and layout, outperforming existing methods.
Text-to-Image Seedream3.0 data annotation pipeline typographic hierarchy paired datasets progressive training strategy multi-task generation usability rate