Summary of Object-centric Diffusion For Efficient Video Editing, by Kumara Kahatapitiya et al.
Object-Centric Diffusion for Efficient Video Editing
by Kumara Kahatapitiya, Adil Karjauv, Davide Abati, Fatih Porikli, Yuki M. Asano, Amirhossein Habibian
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a solution to improve the efficiency of diffusion-based video editing methods. Current approaches can transform videos by altering their global style, local structure, and attributes based on textual edit prompts. However, these solutions often require significant memory and computational resources, which can be a bottleneck for real-world applications. To address this issue, the authors propose two novel techniques: Object-Centric Sampling and Object-Centric Token Merging. These methods allow for a more efficient allocation of computations to foreground regions, which are typically more important for perceptual quality. The results show that these proposals can reduce latency by up to 10x while maintaining comparable synthesis quality. This work has the potential to make diffusion-based video editing more practical and widely applicable. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make video editing faster and better. Currently, it takes a lot of computer power and memory to edit videos using special machines called diffusion models. These models can change the style, structure, or attributes of videos based on text prompts. But this process is slow and uses too much energy. To fix this, the researchers came up with two new ideas: Object-Centric Sampling and Object-Centric Token Merging. These methods allow computers to focus more on changing important parts of the video instead of wasting time on unimportant background regions. The results show that these new techniques can make video editing up to 10 times faster while keeping the same level of quality. |
Keywords
* Artificial intelligence * Diffusion * Token