Summary of Fine-gained Zero-shot Video Sampling, by Dengsheng Chen et al.
Fine-gained Zero-shot Video Sampling
by Dengsheng Chen, Jie Hu, Xiaoming Wei, Enhua Wu
First submitted to arxiv on: 31 Jul 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 proposed Zero-Shot video Sampling algorithm (denoted as ^2) enables direct sampling of high-quality video clips from existing image synthesis methods without training or optimization. Building on dependency noise models and temporal momentum attention, ^2 ensures content consistency and animation coherence, exceling in tasks like conditional and context-specialized video generation and instruction-guided video editing. By leveraging Stable Diffusion and other image-based approaches, this novel method outperforms recent supervised methods in zero-shot video generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to create videos from images without needing big datasets or complicated training. The approach, called Zero-Shot video Sampling algorithm (^2), uses existing image-making techniques and makes them work for short video clips. This means we can get high-quality videos quickly, without having to prepare lots of data or adjust the models. This is useful for things like making personalized videos or editing videos according to instructions. |
Keywords
» Artificial intelligence » Attention » Diffusion » Image synthesis » Optimization » Supervised » Zero shot
