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.

翻译：现实世界的视频创作通常涉及复杂推理工作流：从杂乱素材中筛选相关镜头、规划缺失镜头以完善叙事、并将其组织为连贯的故事线。然而现有基准仅关注独立子任务，缺乏对完整流程的评估支持。为填补这一空白，我们提出多模态上下文到脚本创作（MCSC）这一新任务，将杂乱的多模态输入和用户指令转化为结构化、可执行的视频脚本。我们进一步引入首个大规模MCSC数据集MCSC-Bench，包含11,000余个精心标注的视频。每个样本包含：(1)冗余的多模态素材与用户指令；(2)包含基于素材的镜头、新规划镜头（含拍摄指令）及对齐镜头的画外音在内的连贯、可生产脚本。MCSC-Bench支持对素材筛选、叙事规划、条件化脚本生成的全方位评估，并设有域内与域外测试集。实验表明，当前多模态大模型在长上下文条件下难以进行结构化感知推理，凸显了本基准的挑战性。基于MCSC-Bench训练的模型实现了最优性能（SOTA），其中8B参数模型超越Gemini-2.5-Pro，并能泛化至域外场景。由生成脚本引导的下游视频生成进一步验证了MCSC的实用价值。数据集将很快公开。