AllocMV：通过结构化持久状态实现音乐视频生成的最优资源分配
AgentMV: A State-Guided Multi-Agent Framework for Budget-Aware Music Video Generation
AI总结：
AllocMV提出了一种分层框架，将音乐视频合成建模为多重选择背包问题，通过动态规划优化资源分配，确保跨镜头一致性并降低生成成本。
摘要：
生成长时域音乐视频（MVs）常受限于高昂的计算成本和维持跨镜头一致性困难。我们提出AllocMV，一种分层框架，将音乐视频合成建模为多重选择背包问题（MCKP）。AllocMV将视频的持久状态表示为紧凑的结构化对象，包含角色实体、场景先验和共享图，由全局规划器在实现前生成。通过从多模态线索估计片段显著性，基于动态规划的组级MCKP求解器在高生成、中生成和重用分支间最优分配资源。对于重复的音乐动机，我们实现基于发散的分支策略，通过重用视觉前缀降低成本并确保动机级连续性。通过成本-质量比（CQR）评估，AllocMV在严格预算和节奏约束下实现了感知质量和资源消耗的最佳权衡。
英文摘要：
Generating a complete music video from a song requires more than synthesizing visually plausible clips for individual lyric prompts. A practical system must maintain long-range visual consistency, coordinate recurring motifs, synchronize edits with musical structure, and manage the cumulative cost of video generation. Existing approaches typically generate segments independently or adopt fixed generation strategies, limiting their ability to perform global planning over an entire song. We present AgentMV, a state-guided multi-agent framework for budget-aware music video generation. AgentMV decomposes the production process into specialized agents for music perception, script planning, visual asset provision, segment realization, and final assembly, coordinated through a Structured Persistent State that enables information exchange and state tracking throughout the generation process. To optimize generation resources, we formulate motif-aware segment realization as a group-level Multiple-Choice Knapsack Problem solved via dynamic programming, considering segment importance, generation quality, cost, and motif reuse under a global budget constraint. Experiments on song benchmarks demonstrate that AgentMV improves quality-cost trade-offs over existing MV generation frameworks, highlighting the potential of state-guided multi-agent coordination and budget-aware planning for long-form music video generation with improved quality and efficiency.
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。
