PersonaVlog：基于多智能体协作与迭代自校正的个性化多模态视频博客生成
PersonaVlog: Personalized Multimodal Vlog Generation with Multi-Agent Collaboration and Iterative Self-Correction
AI总结：
针对现有自动化Vlog生成依赖预定义脚本、缺乏动态性与个性化的问题，提出基于MLLMs多智能体协作与迭代自校正的PersonaVlog框架，还推出ThemeVlogEval评估基准，实验验证其性能优于多个基线。
摘要：
随着短视频与个性化内容需求的增长，自动化视频日志（Video Log，Vlog）生成已成为多模态内容创作的关键方向。现有方法大多依赖预定义脚本，缺乏动态性与个人表达，因此亟需一种能实现有效多模态协作且高度个性化的自动化Vlog生成方法。为此，我们提出PersonaVlog——一种自动化多模态风格化Vlog生成框架，可基于给定主题与参考图像，生成包含视频、背景音乐及内心独白语音的个性化Vlog。具体而言，我们提出了基于多模态大语言模型（Multimodal Large Language Models, MLLMs）的多智能体协作框架，该框架能根据用户输入高效生成用于多模态内容创作的高质量提示词，从而提升创作过程的效率与创意性。此外，我们引入了反馈回滚机制，利用MLLMs对生成结果进行评估并提供反馈，实现多模态内容的迭代自校正。我们还提出了ThemeVlogEval——一种基于主题的自动化基准测试框架，为公平评估提供标准化指标与数据集。综合实验表明，我们的框架相较于多个基线模型具有显著优势与潜力，凸显了其在自动化Vlog生成方面的有效性与巨大应用前景。
英文摘要：
With the growing demand for short videos and personalized content, automated Video Log (Vlog) generation has become a key direction in multimodal content creation. Existing methods mostly rely on predefined scripts, lacking dynamism and personal expression. Therefore, there is an urgent need for an automated Vlog generation approach that enables effective multimodal collaboration and high personalization. To this end, we propose PersonaVlog, an automated multimodal stylized Vlog generation framework that can produce personalized Vlogs featuring videos, background music, and inner monologue speech based on a given theme and reference image. Specifically, we propose a multi-agent collaboration framework based on Multimodal Large Language Models (MLLMs). This framework efficiently generates high-quality prompts for multimodal content creation based on user input, thereby improving the efficiency and creativity of the process. In addition, we incorporate a feedback and rollback mechanism that leverages MLLMs to evaluate and provide feedback on generated results, thereby enabling iterative self-correction of multimodal content. We also propose ThemeVlogEval, a theme-based automated benchmarking framework that provides standardized metrics and datasets for fair evaluation. Comprehensive experiments demonstrate the significant advantages and potential of our framework over several baselines, highlighting its effectiveness and great potential for generating automated Vlogs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
