Summary of Raccoon: a Versatile Instructional Video Editing Framework with Auto-generated Narratives, by Jaehong Yoon et al.
RACCooN: A Versatile Instructional Video Editing Framework with Auto-Generated Narratives
by Jaehong Yoon, Shoubin Yu, Mohit Bansal
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes RACCooN, a novel video-to-paragraph-to-video generative framework that enables user-friendly video editing capabilities. The framework consists of two stages: Video-to-Paragraph (V2P) and Paragraph-to-Video (P2V). In the V2P stage, the model automatically generates well-structured natural language descriptions of video scenes, capturing both context and object details. Users can refine these descriptions to guide the video diffusion model, enabling various modifications such as removal, addition, or modification of objects. The proposed approach contributes a multi-granular spatiotemporal pooling strategy for generating structured video descriptions without requiring complex annotations, simplifying precise video content editing based on text. RACCooN also incorporates auto-generated narratives to enhance generated content quality and accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes video editing easier by creating a machine that can understand and change videos. The machine, called RACCooN, can look at a video and write a short description of what’s happening in it. This is helpful because it means people don’t have to write long descriptions for the machine to know what to do with the video. People can also use this machine to make changes to the video, like removing or adding objects, by giving it instructions based on its written description. |
Keywords
» Artificial intelligence » Diffusion model » Spatiotemporal
