Summary of Mofa-video: Controllable Image Animation Via Generative Motion Field Adaptions in Frozen Image-to-video Diffusion Model, by Muyao Niu et al.
MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model
by Muyao Niu, Xiaodong Cun, Xintao Wang, Yong Zhang, Ying Shan, Yinqiang Zheng
First submitted to arxiv on: 30 May 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 paper presents MOFA-Video, a controllable image animation method that generates video from given images using various additional signals or their combinations. This approach differs from previous methods, which were limited to specific motion domains or showed weak control abilities. The authors design domain-aware motion field adapters (MOFA-Adapters) to control the generated motions in the video generation pipeline. MOFA-Adapters consider temporal motion consistency and generate dense motion flows from sparse control conditions. The method is trained on manual trajectories and human landmarks individually, allowing for more controllable video generation when combining adapters from different domains. This work demonstrates a robust and versatile approach to image-to-video synthesis, with potential applications in various fields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MOFA-Video is a new way to turn pictures into videos using extra information like hand movements or facial features. This helps make the animation more realistic and easier to control. The method uses special “adapters” that learn how to move objects and people in the video based on this extra information. These adapters can work together to create more complex animations, making MOFA-Video a powerful tool for generating videos. |
