PosterOmni: Generalized Artistic Poster Creation via Task Distillation and Unified Reward Feedback

2026-02-12

AI Summary

This work addresses the dual challenge of semantic fidelity and aesthetic coherence in image-to-poster generation, which requires balancing localized editing with holistic composition. To this end, we propose the first unified generative paradigm that simultaneously preserves local entities and drives global conceptual design. Our approach integrates multi-task capabilities—including layout control, style guidance, and image prompting—through multi-scenario data distillation, expert knowledge transfer, and a unified reward alignment mechanism. We further introduce PosterOmni-Bench, a comprehensive benchmark for evaluating poster generation systems. Extensive experiments demonstrate that our method significantly outperforms both open-source and proprietary baselines in reference adherence, compositional quality, and aesthetic harmony.

Technology Category

• Natural Language Processing: Generation
• Computer Vision: Computational Photography, Image & Video Synthesis
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

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.

Problem

• image-to-poster generation
• local editing
• global creation
• semantic fidelity
• aesthetic coherence

Innovation

• task distillation
• unified reward feedback
• image-to-poster generation
• multi-task learning
• aesthetic coherence