Summary of V-lasik: Consistent Glasses-removal From Videos Using Synthetic Data, by Rotem Shalev-arkushin et al.
V-LASIK: Consistent Glasses-Removal from Videos Using Synthetic Data
by Rotem Shalev-Arkushin, Aharon Azulay, Tavi Halperin, Eitan Richardson, Amit H. Bermano, Ohad Fried
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 a novel approach to consistent and identity-preserving removal of small attributes in videos, such as glasses, using diffusion-based generative models. The method leverages weakly supervised learning with synthetic imperfect data generated from an adjusted pretrained diffusion model. Despite the imperfections in the generated data, the proposed approach is able to perform the desired edit consistently while preserving the original video content. Furthermore, it demonstrates generalization ability to other local video editing tasks, such as facial sticker-removal. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create more realistic and realistic videos by removing small attributes like glasses from videos. The problem with current methods is that they often make the video look fake or change too much of the original content. This new approach uses a special kind of AI model called a diffusion-based generative model to remove the attribute in a way that looks natural. It works by generating fake data and using it to learn how to edit videos correctly. The result is a method that can successfully remove small attributes from videos while keeping the rest of the content intact. This is an important step forward for video editing, as it allows us to make more realistic and personalized videos. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Generalization » Generative model » Supervised
