arxiv:2506.10741

PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework

Published on Jun 12

· Submitted by

Owen777 on Jun 13

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Authors:

SiXiang Chen ,

Abstract

PosterCraft improves aesthetic poster generation through a unified, modular pipeline with enhanced text rendering, region-aware fine-tuning, aesthetic reinforcement learning, and joint vision-language refinement.

AI-generated summary

Generating aesthetic posters is more challenging than simple design images: it requires not only precise text rendering but also the seamless integration of abstract artistic content, striking layouts, and overall stylistic harmony. To address this, we propose PosterCraft, a unified framework that abandons prior modular pipelines and rigid, predefined layouts, allowing the model to freely explore coherent, visually compelling compositions. PosterCraft employs a carefully designed, cascaded workflow to optimize the generation of high-aesthetic posters: (i) large-scale text-rendering optimization on our newly introduced Text-Render-2M dataset; (ii) region-aware supervised fine-tuning on HQ-Poster100K; (iii) aesthetic-text-reinforcement learning via best-of-n preference optimization; and (iv) joint vision-language feedback refinement. Each stage is supported by a fully automated data-construction pipeline tailored to its specific needs, enabling robust training without complex architectural modifications. Evaluated on multiple experiments, PosterCraft significantly outperforms open-source baselines in rendering accuracy, layout coherence, and overall visual appeal-approaching the quality of SOTA commercial systems. Our code, models, and datasets can be found in the Project page: https://ephemeral182.github.io/PosterCraft

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Owen777

Paper submitter about 21 hours ago

Paper：https://arxiv.org/abs/2506.10741
Github：https://github.com/Ephemeral182/PosterCraft
Project Page：https://ephemeral182.github.io/PosterCraft/
Huggingface: https://huggingface.co/PosterCraft
YouTube: https://www.youtube.com/watch?v=92wMU4D7qx0

Owen777

Paper submitter about 21 hours ago

Abs: Generating aesthetic posters is more challenging than simple design images: it requires not only precise text rendering but also the seamless integration of abstract artistic content, striking layouts, and overall stylistic harmony. To address this, we propose PosterCraft, a unified framework that abandons prior modular pipelines and rigid, predefined layouts, allowing the model to freely explore coherent, visually compelling compositions. PosterCraft employs a carefully designed, cascaded workflow to optimize the generation of high-aesthetic posters: (i) large-scale text-rendering optimization on our newly introduced Text-Render-2M dataset; (ii) region-aware supervised fine-tuning on HQ-Poster-100K; (iii) aesthetic-text reinforcement learning via best-of-n preference optimization; and (iv) joint vision–language feedback refinement. Each stage is supported by a fully automated data-construction pipeline tailored to its specific needs, enabling robust training without complex architectural modifications. Evaluated on multiple experiments, PosterCraft significantly outperforms open-source baselines in rendering accuracy, layout coherence, and overall visual appeal—approaching the quality of SOTA commercial systems. Our code, models, and datasets can be found in the project page: https://ephemeral182.github.io/PosterCraft