LLMPopcorn: Exploring LLMs as Assistants for Popular Micro-video Generation
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910000314580992 |
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| author | Fu, Junchen Ge, Xuri Zheng, Kaiwen Karatzoglou, Alexandros Arapakis, Ioannis Xin, Xin Ni, Yongxin Jose, Joemon M. |
| author_facet | Fu, Junchen Ge, Xuri Zheng, Kaiwen Karatzoglou, Alexandros Arapakis, Ioannis Xin, Xin Ni, Yongxin Jose, Joemon M. |
| contents | In an era where micro-videos dominate platforms like TikTok and YouTube, AI-generated content is nearing cinematic quality. The next frontier is using large language models (LLMs) to autonomously create viral micro-videos, a largely untapped potential that could shape the future of AI-driven content creation. To address this gap, this paper presents the first exploration of LLM-assisted popular micro-video generation (LLMPopcorn). We selected popcorn as the icon for this paper because it symbolizes leisure and entertainment, aligning with this study on leveraging LLMs as assistants for generating popular micro-videos that are often consumed during leisure time. Specifically, we empirically study the following research questions: (i) How can LLMs be effectively utilized to assist popular micro-video generation? (ii) To what extent can prompt-based enhancements optimize the LLM-generated content for higher popularity? (iii) How well do various LLMs and video generators perform in the popular micro-video generation task? Exploring these questions, we show that advanced LLMs like DeepSeek-V3 can generate micro-videos with popularity rivaling human content. Prompt enhancement further boosts results, while benchmarking highlights DeepSeek-V3 and R1 for LLMs, and LTX-Video and HunyuanVideo for video generation. This work advances AI-assisted micro-video creation and opens new research directions. The code is publicly available at https://github.com/GAIR-Lab/LLMPopcorn. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12945 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLMPopcorn: Exploring LLMs as Assistants for Popular Micro-video Generation Fu, Junchen Ge, Xuri Zheng, Kaiwen Karatzoglou, Alexandros Arapakis, Ioannis Xin, Xin Ni, Yongxin Jose, Joemon M. Computation and Language Computer Vision and Pattern Recognition In an era where micro-videos dominate platforms like TikTok and YouTube, AI-generated content is nearing cinematic quality. The next frontier is using large language models (LLMs) to autonomously create viral micro-videos, a largely untapped potential that could shape the future of AI-driven content creation. To address this gap, this paper presents the first exploration of LLM-assisted popular micro-video generation (LLMPopcorn). We selected popcorn as the icon for this paper because it symbolizes leisure and entertainment, aligning with this study on leveraging LLMs as assistants for generating popular micro-videos that are often consumed during leisure time. Specifically, we empirically study the following research questions: (i) How can LLMs be effectively utilized to assist popular micro-video generation? (ii) To what extent can prompt-based enhancements optimize the LLM-generated content for higher popularity? (iii) How well do various LLMs and video generators perform in the popular micro-video generation task? Exploring these questions, we show that advanced LLMs like DeepSeek-V3 can generate micro-videos with popularity rivaling human content. Prompt enhancement further boosts results, while benchmarking highlights DeepSeek-V3 and R1 for LLMs, and LTX-Video and HunyuanVideo for video generation. This work advances AI-assisted micro-video creation and opens new research directions. The code is publicly available at https://github.com/GAIR-Lab/LLMPopcorn. |
| title | LLMPopcorn: Exploring LLMs as Assistants for Popular Micro-video Generation |
| topic | Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.12945 |