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arXiv · 2605.30090

DirectorBench: Diagnosing Long-Form Video Generation with Personalized Multi-Agent Evaluation

Jiamin Chen·Qianben Chen·Jiawen Zhang·Yidi Wu·Yuchen Li·Xiaokun Zhang·Wangchunshu Zhou·Chen Ma
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Abstract

Long-form video generation is rapidly moving from short, single-scene synthesis toward minute-long, multi-shot creation with narrative structure, cinematic control, audio, and cross-modal synchronization. However, evaluating such videos remains challenging, since existing benchmarks largely focus on local visual quality, short-horizon temporal consistency, or generic prompt alignment, and provide limited diagnosis of workflow failures and user-dependent preferences. We introduce DirectorBench, a personalized multi-agent diagnostic benchmark for long-form video generation. DirectorBench evaluates generated videos with respect to 80 structured metadata entries, 7 user profiles, and 40 checkpoint criteria across 5 dimensions: script, visual, audio, cross-modal, and stability. Instead of reducing quality to a single aggregate score, DirectorBench localizes checkpoint-level bottlenecks and supports profile-aware evaluation. We evaluate 4 long-form video generation workflows, 6 base LLMs, and 7 user profiles. Across workflows, DirectorBench reveals a between-unit bottleneck: transition quality averages only 0.256 and reaches 0.356 for the best workflow, while prompt-level user demand fulfillment averages 0.71. We further conduct human evaluation with 14 annotators to validate the alignment between DirectorBench and human judgment. The results show that DirectorBench captures human-perceptible quality differences and reveals workflow- and profile-dependent failure modes that are hidden by aggregate scoring. These findings highlight the importance of diagnostic and profile-aware benchmarking for long-form video generation.

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Jiamin Chen, Qianben Chen, Jiawen Zhang, Yidi Wu, Yuchen Li, Xiaokun Zhang, Wangchunshu Zhou, Chen Ma. 2026-05-28. DirectorBench: Diagnosing Long-Form Video Generation with Personalized Multi-Agent Evaluation. https://arxiv.org/abs/2605.30090

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