OmniScript: Towards Audio-Visual Script Generation for Long-Form Cinematic Video

Current multimodal large language models (MLLMs) havedemonstrated remarkable capabilities in short-form video understand-ing, yet translating long-form cinematic videos into detailed, temporallygrounded scripts remains a significant challenge. This paper introducesthe novel video-to-script (V2S) task, aiming to generate hierarchical,scene-by-scene scripts encompassing character actions, dialogues, expres-sions, and audio cues. To facilitate this, we construct a first-of-its-kindhuman-annotated benchmark and propose a temporally-aware hierarchi-cal evaluation framework. Furthermore, we present OmniScript, an 8B-parameter omni-modal (audio-visual) language model tailored for long-form narrative comprehension. OmniScript is trained via a progressivepipeline that leverages chain-of-thought supervised fine-tuning for plotand character reasoning, followed by reinforcement learning using tem-porally segmented rewards. Extensive experiments demonstrate that de-spite its parameter efficiency, OmniScript significantly outperforms largeropen-source models and achieves performance comparable to state-of-the-art proprietary models, including Gemini 3-Pro, in both temporallocalization and multi-field semantic accuracy. The code is available athttps://github.com/TencentARC/OmniScript.