OmniScript: Towards Audio-Visual Script Generation for Long-Form Cinematic Video
Abstract
Current multimodal large language models (MLLMs) have demonstrated remarkable capabilities in short-form video understanding, yet translating long-form cinematic videos into detailed, temporally grounded scripts remains a significant challenge. This paper introduces the novel video-to-script (V2S) task, aiming to generate hierarchical, scene-by-scene scripts encompassing character actions, dialogues, expressions, and audio cues. To facilitate this, we construct a first-of-its-kind human-annotated benchmark and propose a temporally-aware hierarchical 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 progressive pipeline that leverages chain-of-thought supervised fine-tuning for plot and character reasoning, followed by reinforcement learning using temporally segmented rewards. Extensive experiments demonstrate that despite its parameter efficiency, OmniScript significantly outperforms larger open-source models and achieves performance comparable to state-of-the-art proprietary models, including Gemini 3-Pro, in both temporal localization and multi-field semantic accuracy.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of OmniScript, a novel framework for generating detailed scripts from long-form cinematic videos, which significantly advances the state of the art in multimodal understanding. The comprehensive methodology, extensive experimental validation, and potential for real-world applications underscore its significance in the field of machine learning and computational media analysis.
Methodology
The methodology presented in the paper is robust and innovative, addressing the complex task of generating hierarchical scripts from long-form cinematic videos. The authors introduce a novel video-to-script (V2S) task and develop a comprehensive framework that includes a human-annotated benchmark, a temporally-aware evaluation framework, and the OmniScript model. The use of a memory-augmented annotation pipeline and a chain-of-thought reasoning approach for script generation is particularly noteworthy, as it enhances the model's ability to maintain narrative coherence across long sequences. The integration of audio-visual modalities and the reinforcement learning strategy for fine-tuning further exemplify the sophistication of the proposed methods.
Results & evaluation
The experiments are extensive and well-structured, demonstrating the effectiveness of the OmniScript model against both proprietary and open-source models. The results show significant improvements in both event-level understanding and temporal localization, with the model achieving competitive performance despite its smaller parameter size. The authors provide a thorough comparison across various metrics and video lengths, showcasing the model's robustness in handling long-form content. However, the paper could benefit from additional qualitative evaluations to complement the quantitative results.
Reproducibility
The paper provides detailed implementation details, including the training pipeline and the datasets used. However, the absence of a publicly available code repository limits the reproducibility of the results. While the authors describe their methodology comprehensively, access to the actual model and datasets would enhance the ability of other researchers to replicate and build upon this work.
Limitations
One limitation is the reliance on human-annotated data, which can be labor-intensive and may introduce biases. Additionally, while the model shows promise, its performance on extremely long videos (beyond 30 minutes) appears to degrade, indicating potential challenges in maintaining narrative coherence over extended durations. The paper also does not address the computational costs associated with training and deploying such a large model.
Broader impact
The potential applications of this research are significant, particularly in the film and television industry, where automated script generation could streamline content creation processes. The work also opens avenues for further research in multimodal understanding and narrative generation, which could have implications in various domains such as education, entertainment, and accessibility. The main contribution of this paper is the introduction of OmniScript, a novel framework for generating detailed scripts from long-form cinematic videos, which significantly advances the state of the art in multimodal understanding. The comprehensive methodology, extensive experimental validation, and potential for real-world applications underscore its significance in the field of machine learning and computational media analysis.
Author of this paper? Send a correction.