MovieLLM: Enhancing Long Video Understanding with AI-Generated Movies (2403.01422v2)

Abstract: Development of multimodal models has marked a significant step forward in how machines understand videos. These models have shown promise in analyzing short video clips. However, when it comes to longer formats like movies, they often fall short. The main hurdles are the lack of high-quality, diverse video data and the intensive work required to collect or annotate such data. In face of these challenges, we propose MovieLLM, a novel framework designed to synthesize consistent and high-quality video data for instruction tuning. The pipeline is carefully designed to control the style of videos by improving textual inversion technique with powerful text generation capability of GPT-4. As the first framework to do such thing, our approach stands out for its flexibility and scalability, empowering users to create customized movies with only one description. This makes it a superior alternative to traditional data collection methods. Our extensive experiments validate that the data produced by MovieLLM significantly improves the performance of multimodal models in understanding complex video narratives, overcoming the limitations of existing datasets regarding scarcity and bias.

• The paper introduces MovieLLM, a framework that uses AI-generated synthetic data to improve comprehension of long video formats by generating coherent movie plots and visuals.
• It employs a multi-stage pipeline using GPT-4 for plot generation, style immobilization via textual inversion, and keyframe production for robust data creation.
• Experimental results show that MovieLLM-enhanced models outperform baselines on zero-shot video QA tasks and long video comprehension benchmarks across 15 movie genres.

Overview of "MovieLLM: Enhancing Long Video Understanding with AI-Generated Movies"

This paper introduces MovieLLM, a novel framework designed to improve the understanding of long video formats, such as full-length movies, by leveraging synthetic data. The framework addresses the limitations of existing multimodal LLMs that struggle with long-duration content due to a scarcity of high-quality, diverse datasets. MovieLLM seeks to overcome these challenges by utilizing GPT-4 and text-to-image (T2I) models to generate detailed, consistent scripts and visuals, thereby creating a flexible and scalable data generation pipeline.

Methodology

The methodology of MovieLLM involves three main stages:

1. Movie Plot Generation:
   • Utilizes GPT-4 to generate diverse and coherent movie plots by defining specific elements such as themes, overviews, styles, characters, and key frame descriptions.
   • Implements a story expansion strategy to mitigate LLM forgetting issues, dividing the plot into epoch chapters, narrative threads, and frame descriptions.
2. Style Immobilization Process:
   • Employs textual inversion to convert style descriptions into stable diffusion model embeddings, guiding it to generate scenes with consistent styles.
3. Video Instruction Data Generation:
   • Generates key frames using style embeddings and celebrity-wise character representations along with enriching QA pairs derived from the movie plots.

Experimental Validation

The paper reports strong experimental results, asserting that MovieLLM significantly enhances the performance of multimodal models in understanding complex video narratives. The dataset generated using MovieLLM demonstrates robustness in addressing issues of scarcity and bias prevalent in existing data.

Results and Implications

MovieLLM's resulting dataset supports 15 different movie genres, highlighting its capability to foster diverse video understanding scenarios. In comparative evaluations, models fine-tuned with data from MovieLLM outperform baselines on zero-shot video QA tasks and long video comprehension benchmarks, suggesting improvements in temporal, plot, and overview understanding.

Future Directions

By automating the generation of long video datasets, this approach reduces dependence on costly manual annotation processes, thus broadening scalability for training robust multimodal models. The paper speculates that integrating advanced diffusion models for enhanced video data generation could further expand MovieLLM's applicability, facilitating future developments in video understanding via AI.

Conclusion

MovieLLM presents a significant advancement in the automatic generation of high-quality data for long video comprehension by synthesizing intricate sequences of visual and textual data. Its methodology not only reduces manual labor but also enables the creation of large, diverse datasets necessary for training sophisticated multimodal models, potentially reshaping approaches to AI-driven video understanding in the future.