PosterCraft：在統一框架下重新思考高品質美學海報生成

摘要

生成美觀的海報比簡單的設計圖像更具挑戰性：它不僅需要精確的文字渲染，還需要無縫整合抽象的藝術內容、引人注目的佈局以及整體的風格和諧。為此，我們提出了PosterCraft，這是一個統一的框架，摒棄了先前的模組化流程和僵化的預定義佈局，使模型能夠自由探索連貫且視覺上引人入勝的構圖。PosterCraft採用精心設計的級聯工作流程來優化高美感海報的生成：(i) 在我們新引入的Text-Render-2M數據集上進行大規模文字渲染優化； (ii) 在HQ-Poster100K上進行區域感知的監督微調； (iii) 通過最佳-n偏好優化進行美學文字強化學習； (iv) 聯合視覺-語言反饋精煉。每個階段都得到了一個完全自動化的數據構建流程的支持，該流程根據其特定需求進行了定制，從而實現了無需複雜架構修改的穩健訓練。在多項實驗中進行評估後，PosterCraft在渲染準確性、佈局連貫性和整體視覺吸引力方面顯著優於開源基準，接近 SOTA商業系統的質量。我們的代碼、模型和數據集可以在項目頁面找到： https://ephemeral182.github.io/PosterCraft

English

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

PosterCraft：在統一框架下重新思考高品質美學海報生成

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

摘要

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