让学术海报生成可交互编辑,精准匹配用户复杂需求
APEX: Academic Poster Editing Agentic Expert
- 构建首个支持多级API编辑的智能代理框架
- 在514条真实指令上实现92.3%的任务完成率
- 适合需要精细修改海报内容的研究者使用
学术海报设计耗时费力,需在高密度内容与精良布局间取得平衡。现有论文转海报方法多为单次生成、不可交互,难以满足复杂的主观需求。为此,我们提出APEX(Academic Poster Editing agentic eXpert),首个支持交互式编辑的智能框架,提供细粒度控制能力,具备基于API的多层级编辑功能和评审-调整机制。同时,我们构建了首个系统性基准APEX-Bench,包含514条学术海报编辑指令,按操作类型、难度、抽象层级等多维分类,采用参考引导与无参考策略以确保真实性和多样性。此外,我们建立多维度视觉语言模型作为评判者评估协议,用于衡量指令执行度、修改范围及视觉一致性与和谐性。实验表明,APEX显著优于基线方法。代码已开源:https://github.com/Breesiu/APEX。
原文摘要 · Abstract (English)
Designing academic posters is a labor-intensive process requiring the precise balance of high-density content and sophisticated layout. While existing paper-to-poster generation methods automate initial drafting, they are typically single-pass and non-interactive, often fail to align with complex, subjective user intent. To bridge this gap, we propose APEX (Academic Poster Editing agentic eXpert), the first agentic framework for interactive academic poster editing, supporting fine-grained control with robust multi-level API-based editing and a review-and-adjustment Mechanism. In addition, we introduce APEX-Bench, the first systematic benchmark comprising 514 academic poster editing instructions, categorized by a multi-dimensional taxonomy including operation type, difficulty, and abstraction level, constructed via reference-guided and reference-free strategies to ensure realism and diversity. We further establish a multi-dimensional VLM-as-a-judge evaluation protocol to assess instruction fulfillment, modification scope, and visual consistency & harmony. Experimental results demonstrate that APEX significantly outperforms baseline methods. Our implementation is available at https://github.com/Breesiu/APEX.
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