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Intelligent Director: An Automatic Framework for Dynamic Visual Composition using ChatGPT

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arxiv 2402.15746 v1 pith:6MGV24I3 submitted 2024-02-24 cs.CV cs.AIcs.MM

classification cs.CVcs.AIcs.MM
keywords musicvideovideosframeworkintelligentcaptionschatgptcomposition
verification ladder T0 review T1 audit T2 compute T3 formal
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With the rise of short video platforms represented by TikTok, the trend of users expressing their creativity through photos and videos has increased dramatically. However, ordinary users lack the professional skills to produce high-quality videos using professional creation software. To meet the demand for intelligent and user-friendly video creation tools, we propose the Dynamic Visual Composition (DVC) task, an interesting and challenging task that aims to automatically integrate various media elements based on user requirements and create storytelling videos. We propose an Intelligent Director framework, utilizing LENS to generate descriptions for images and video frames and combining ChatGPT to generate coherent captions while recommending appropriate music names. Then, the best-matched music is obtained through music retrieval. Then, materials such as captions, images, videos, and music are integrated to seamlessly synthesize the video. Finally, we apply AnimeGANv2 for style transfer. We construct UCF101-DVC and Personal Album datasets and verified the effectiveness of our framework in solving DVC through qualitative and quantitative comparisons, along with user studies, demonstrating its substantial potential.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Making Your Dreams A Reality: Decoding the Dreams into a Coherent Video Story from fMRI Signals

    cs.CV 2025-01 reject novelty 5.0 of 10

    A zero-shot pipeline claims to generate dream videos from sleep fMRI by transferring a model trained on awake visual perception, with only three dream segments validated.

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