Summary of Motionflow: Attention-driven Motion Transfer in Video Diffusion Models, by Tuna Han Salih Meral et al.
MotionFlow: Attention-Driven Motion Transfer in Video Diffusion Models
by Tuna Han Salih Meral, Hidir Yesiltepe, Connor Dunlop, Pinar Yanardag
First submitted to arxiv on: 6 Dec 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 framework, MotionFlow, aims to improve the fine-grained control of motion patterns in text-to-video models by utilizing cross-attention maps. This enables seamless motion transfers across various contexts without requiring retraining. The method leverages pre-trained video diffusion models and outperforms existing approaches in both fidelity and versatility, particularly during drastic scene alterations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MotionFlow is a new way to control the movement in videos made using artificial intelligence (AI). Right now, these AI systems can create lots of different and interesting videos. But they don’t have much control over how things move. This makes it hard to use them in real-life situations. The MotionFlow system helps fix this problem by allowing for more precise control over the movement in videos. |
Keywords
» Artificial intelligence » Cross attention
