Summary of Vivid-10m: a Dataset and Baseline For Versatile and Interactive Video Local Editing, by Jiahao Hu et al.
VIVID-10M: A Dataset and Baseline for Versatile and Interactive Video Local Editing
by Jiahao Hu, Tianxiong Zhong, Xuebo Wang, Boyuan Jiang, Xingye Tian, Fei Yang, Pengfei Wan, Di Zhang
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper introduces a dataset and model for video local editing, addressing challenges in constructing large-scale datasets and training models. The proposed VIVID-10M dataset is a hybrid image-video local editing benchmark containing 9.7M samples, reducing data construction and model training costs. The accompanying VIVID model supports entity addition, modification, and deletion, as well as a keyframe-guided interactive video editing mechanism. This enables users to iteratively edit keyframes and propagate changes to other frames, minimizing latency. The paper demonstrates state-of-the-art performance in video local editing through extensive experiments, surpassing baseline methods in both automated metrics and user studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make video editing easier by creating a large dataset of real-world videos and a special computer program that can edit these videos. The problem with current video editing is that it’s hard to find datasets for this task, making it difficult to train computers to do the job well. The new dataset has millions of examples of different video editing tasks, which will help computers learn how to edit videos better. The computer program, called VIVID, lets users make changes to keyframes in a video and then applies those changes to other parts of the video. This makes it easier for people to edit their own videos. |
