Summary of Semantically Consistent Video Inpainting with Conditional Diffusion Models, by Dylan Green et al.
Semantically Consistent Video Inpainting with Conditional Diffusion Models
by Dylan Green, William Harvey, Saeid Naderiparizi, Matthew Niedoba, Yunpeng Liu, Xiaoxuan Liang, Jonathan Lavington, Ke Zhang, Vasileios Lioutas, Setareh Dabiri, Adam Scibior, Berend Zwartsenberg, Frank Wood
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 presents a novel framework for video inpainting using conditional video diffusion models. It reframes video inpainting as a conditional generative modeling problem, introducing sampling schemes to capture long-range dependencies in the context. The method conditions on known pixels in incomplete frames and is capable of generating diverse, high-quality inpaintings and synthesizing new content that is spatially, temporally, and semantically consistent with the provided context. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video inpainting is a task that requires filling in missing parts of a video by creating new information. Current methods use techniques like optical flow or attention-based approaches to do this. However, these methods struggle when they need to create new content that isn’t present in other frames. The paper proposes a new way to approach video inpainting using something called conditional generative models. This method uses a different technique to fill in the missing parts and can generate more diverse and realistic results. |
Keywords
» Artificial intelligence » Attention » Diffusion » Optical flow
