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arXiv · 2507.13285

Multi-Agent Synergy-Driven Iterative Visual Narrative Synthesis

Wang Xi·Quan Shi·Tian Yu·Yujie Peng·Jiayi Sun·Mengxing Ren·Zenghui Ding·Ningguang Yao
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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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Wang Xi, Quan Shi, Tian Yu, Yujie Peng, Jiayi Sun, Mengxing Ren, Zenghui Ding, Ningguang Yao. 2025-07-17. Multi-Agent Synergy-Driven Iterative Visual Narrative Synthesis. https://arxiv.org/abs/2507.13285

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