**MovieLLM: Enhancing Long Video Understanding with AI-Generated Movies** **Authors:** Zhende Song, Chenchen Wang, Jiamu Sheng, Chi Zhang, Gang Yu, Jiayuan Fan, Tao Chen **Institution:** Fudan University, Tencent PCG **Abstract:** The development of multimodal models has significantly advanced how machines understand videos, particularly in analyzing short clips. However, these models often fall short when dealing with longer formats like movies due to the lack of high-quality, diverse video data and the intensive work required to collect or annotate such data. To address this, the authors 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 techniques with powerful text generation capabilities of GPT-4. As the first framework to do such a thing, MovieLLM stands out for its flexibility and scalability, allowing users to create customized movies with only one description. 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. **Key Contributions:** - Develop a novel pipeline for generating movie-level video instruction tuning datasets by combining GPT-4 and textual inversion. - Develop and release a comprehensive dataset for movie-level video understanding, along with a sophisticated model trained for enhanced understanding of long videos. - Propose a benchmark dataset for evaluating long video comprehension capabilities, showing significant improvements in model performance. **Methodology:** The MovieLLM pipeline consists of three main stages: 1. **Movie Plot Generation:** Uses GPT-4 to generate diverse and compelling movie plots based on specific elements like themes, overview, and styles. 2. **Style Immobilization Process:** Utilizes textual inversion to immobilize style descriptions into the latent space of a diffusion model, guiding it to generate scenes with consistent style while maintaining diversity. 3. **Video Instruction Data Generation:** Integrates the generative capabilities of GPT-4 with the developed style-guided diffusion model to produce style-consistent key frames and corresponding QA pairs. **Experiments:** - Extensive experiments validate the effectiveness of the MovieLLM-generated data, showing superior performance in key frame quality, video understanding, and video-based generative performance benchmarks. - Ablation studies demonstrate the impact of varying proportions of the MovieLLM dataset on model performance and its efficacy compared to MovieNet. **Ethics:** The authors reflect on the broader impacts and ethical considerations associated with MovieLLM, emphasizing privacy, security, accessibility, employment, sustainability, and potential misuse. **Conclusion:** MovieLLM significantly enhances the understanding of long videos by generating rich and diverse datasets, making a significant contribution to the advancement of multimodal models.**MovieLLM: Enhancing Long Video Understanding with AI-Generated Movies** **Authors:** Zhende Song, Chenchen Wang, Jiamu Sheng, Chi Zhang, Gang Yu, Jiayuan Fan, Tao Chen **Institution:** Fudan University, Tencent PCG **Abstract:** The development of multimodal models has significantly advanced how machines understand videos, particularly in analyzing short clips. However, these models often fall short when dealing with longer formats like movies due to the lack of high-quality, diverse video data and the intensive work required to collect or annotate such data. To address this, the authors 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 techniques with powerful text generation capabilities of GPT-4. As the first framework to do such a thing, MovieLLM stands out for its flexibility and scalability, allowing users to create customized movies with only one description. 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. **Key Contributions:** - Develop a novel pipeline for generating movie-level video instruction tuning datasets by combining GPT-4 and textual inversion. - Develop and release a comprehensive dataset for movie-level video understanding, along with a sophisticated model trained for enhanced understanding of long videos. - Propose a benchmark dataset for evaluating long video comprehension capabilities, showing significant improvements in model performance. **Methodology:** The MovieLLM pipeline consists of three main stages: 1. **Movie Plot Generation:** Uses GPT-4 to generate diverse and compelling movie plots based on specific elements like themes, overview, and styles. 2. **Style Immobilization Process:** Utilizes textual inversion to immobilize style descriptions into the latent space of a diffusion model, guiding it to generate scenes with consistent style while maintaining diversity. 3. **Video Instruction Data Generation:** Integrates the generative capabilities of GPT-4 with the developed style-guided diffusion model to produce style-consistent key frames and corresponding QA pairs. **Experiments:** - Extensive experiments validate the effectiveness of the MovieLLM-generated data, showing superior performance in key frame quality, video understanding, and video-based generative performance benchmarks. - Ablation studies demonstrate the impact of varying proportions of the MovieLLM dataset on model performance and its efficacy compared to MovieNet. **Ethics:** The authors reflect on the broader impacts and ethical considerations associated with MovieLLM, emphasizing privacy, security, accessibility, employment, sustainability, and potential misuse. **Conclusion:** MovieLLM significantly enhances the understanding of long videos by generating rich and diverse datasets, making a significant contribution to the advancement of multimodal models.