Multi-Agent Synergy-Driven Iterative Visual Narrative Synthesis

• URL: http://arxiv.org/abs/2507.13285v1
• Date: Thu, 17 Jul 2025 16:50:07 GMT
• Title: Multi-Agent Synergy-Driven Iterative Visual Narrative Synthesis
• Authors: Wang Xi, Quan Shi, Tian Yu, Yujie Peng, Jiayi Sun, Mengxing Ren, Zenghui Ding, Ningguang Yao,
• Abstract summary: We introduce RCPS, a novel framework for automated generation of high-quality media presentations.<n>We also propose PREVAL, a preference-based evaluation framework to assess presentation quality across Content, Coherence, and Design.<n>PREVAL shows strong correlation with human judgments, validating it as a reliable automated tool for assessing presentation quality.
• Score: 2.846897538377738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated generation of high-quality media presentations is challenging, requiring robust content extraction, narrative planning, visual design, and overall quality optimization. Existing methods often produce presentations with logical inconsistencies and suboptimal layouts, thereby struggling to meet professional standards. To address these challenges, we introduce RCPS (Reflective Coherent Presentation Synthesis), a novel framework integrating three key components: (1) Deep Structured Narrative Planning; (2) Adaptive Layout Generation; (3) an Iterative Optimization Loop. Additionally, we propose PREVAL, a preference-based evaluation framework employing rationale-enhanced multi-dimensional models to assess presentation quality across Content, Coherence, and Design. Experimental results demonstrate that RCPS significantly outperforms baseline methods across all quality dimensions, producing presentations that closely approximate human expert standards. PREVAL shows strong correlation with human judgments, validating it as a reliable automated tool for assessing presentation quality.

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