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arXiv · 2604.15127

MCSC-Bench: Multimodal Context-to-Script Creation for Realistic Video Production

Huanran Hu·Zihui Ren·Dingyi Yang·Liangyu Chen·Qixiang Gao·Tiezheng Ge·Qin Jin
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Abstract

Real-world video creation often involves a complex reasoning workflow of selecting relevant shots from noisy materials, planning missing shots for narrative completeness, and organizing them into coherent storylines. However, existing benchmarks focus on isolated sub-tasks and lack support for evaluating this full process. To address this gap, we propose Multimodal Context-to-Script Creation (MCSC), a new task that transforms noisy multimodal inputs and user instructions into structured, executable video scripts. We further introduce MCSC-Bench, the first large-scale MCSC dataset, comprising 11K+ well-annotated videos. Each sample includes: (1) redundant multimodal materials and user instructions; (2) a coherent, production-ready script containing material-based shots, newly planned shots (with shooting instructions), and shot-aligned voiceovers. MCSC-Bench supports comprehensive evaluation across material selection, narrative planning, and conditioned script generation, and includes both in-domain and out-of-domain test sets. Experiments show that current multimodal LLMs struggle with structure-aware reasoning under long contexts, highlighting the challenges posed by our benchmark. Models trained on MCSC-Bench achieve SOTA performance, with an 8B model surpassing Gemini-2.5-Pro, and generalize to out-of-domain scenarios. Downstream video generation guided by the generated scripts further validates the practical value of MCSC. Datasets will be public soon.

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Huanran Hu, Zihui Ren, Dingyi Yang, Liangyu Chen, Qixiang Gao, Tiezheng Ge, Qin Jin. 2026-04-16. MCSC-Bench: Multimodal Context-to-Script Creation for Realistic Video Production. https://arxiv.org/abs/2604.15127

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