Summary of Motionshop: Zero-shot Motion Transfer in Video Diffusion Models with Mixture Of Score Guidance, by Hidir Yesiltepe et al.
MotionShop: Zero-Shot Motion Transfer in Video Diffusion Models with Mixture of Score Guidance
by Hidir Yesiltepe, Tuna Han Salih Meral, 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 This paper proposes a novel approach to motion transfer in diffusion transformer models using Mixture of Score Guidance (MSG). The authors reformulate conditional score to decompose motion and content scores, enabling creative scene transformations while preserving scene composition. This framework operates directly on pre-trained video diffusion models without additional training or fine-tuning. The authors demonstrate the effectiveness of MSG through extensive experiments, showcasing successful handling of diverse scenarios including single-object, multi-object, and complex camera motion transfer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to move things around in videos using special computer models. It’s like taking a piece of a video and putting it into another video, but making sure the new video looks natural. The authors came up with a clever way to make this work by breaking down what makes something move into two parts: the thing moving and the background. They tested their idea on lots of different videos and showed that it works well for many kinds of movements. This could be useful for movies, TV shows, or even video games. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning » Transformer
