PosterOmni: Generalized Artistic Poster Creation via Task Distillation and Unified Reward Feedback
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2026
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| _version_ | 1866911443557810176 |
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| author | Chen, Sixiang Lai, Jianyu Gao, Jialin Shi, Hengyu Liu, Zhongying Ye, Tian Luo, Junfeng Wei, Xiaoming Zhu, Lei |
| author_facet | Chen, Sixiang Lai, Jianyu Gao, Jialin Shi, Hengyu Liu, Zhongying Ye, Tian Luo, Junfeng Wei, Xiaoming Zhu, Lei |
| contents | Image-to-poster generation is a high-demand task requiring not only local adjustments but also high-level design understanding. Models must generate text, layout, style, and visual elements while preserving semantic fidelity and aesthetic coherence. The process spans two regimes: local editing, where ID-driven generation, rescaling, filling, and extending must preserve concrete visual entities; and global creation, where layout- and style-driven tasks rely on understanding abstract design concepts. These intertwined demands make image-to-poster a multi-dimensional process coupling entity-preserving editing with concept-driven creation under image-prompt control. To address these challenges, we propose PosterOmni, a generalized artistic poster creation framework that unlocks the potential of a base edit model for multi-task image-to-poster generation. PosterOmni integrates the two regimes, namely local editing and global creation, within a single system through an efficient data-distillation-reward pipeline: (i) constructing multi-scenario image-to-poster datasets covering six task types across entity-based and concept-based creation; (ii) distilling knowledge between local and global experts for supervised fine-tuning; and (iii) applying unified PosterOmni Reward Feedback to jointly align visual entity-preserving and aesthetic preference across all tasks. Additionally, we establish PosterOmni-Bench, a unified benchmark for evaluating both local editing and global creation. Extensive experiments show that PosterOmni significantly enhances reference adherence, global composition quality, and aesthetic harmony, outperforming all open-source baselines and even surpassing several proprietary systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_12127 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PosterOmni: Generalized Artistic Poster Creation via Task Distillation and Unified Reward Feedback Chen, Sixiang Lai, Jianyu Gao, Jialin Shi, Hengyu Liu, Zhongying Ye, Tian Luo, Junfeng Wei, Xiaoming Zhu, Lei Computer Vision and Pattern Recognition Image-to-poster generation is a high-demand task requiring not only local adjustments but also high-level design understanding. Models must generate text, layout, style, and visual elements while preserving semantic fidelity and aesthetic coherence. The process spans two regimes: local editing, where ID-driven generation, rescaling, filling, and extending must preserve concrete visual entities; and global creation, where layout- and style-driven tasks rely on understanding abstract design concepts. These intertwined demands make image-to-poster a multi-dimensional process coupling entity-preserving editing with concept-driven creation under image-prompt control. To address these challenges, we propose PosterOmni, a generalized artistic poster creation framework that unlocks the potential of a base edit model for multi-task image-to-poster generation. PosterOmni integrates the two regimes, namely local editing and global creation, within a single system through an efficient data-distillation-reward pipeline: (i) constructing multi-scenario image-to-poster datasets covering six task types across entity-based and concept-based creation; (ii) distilling knowledge between local and global experts for supervised fine-tuning; and (iii) applying unified PosterOmni Reward Feedback to jointly align visual entity-preserving and aesthetic preference across all tasks. Additionally, we establish PosterOmni-Bench, a unified benchmark for evaluating both local editing and global creation. Extensive experiments show that PosterOmni significantly enhances reference adherence, global composition quality, and aesthetic harmony, outperforming all open-source baselines and even surpassing several proprietary systems. |
| title | PosterOmni: Generalized Artistic Poster Creation via Task Distillation and Unified Reward Feedback |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.12127 |