Summary of Reenact Anything: Semantic Video Motion Transfer Using Motion-textual Inversion, by Manuel Kansy et al.
Reenact Anything: Semantic Video Motion Transfer Using Motion-Textual Inversion
by Manuel Kansy, Jacek Naruniec, Christopher Schroers, Markus Gross, Romann M. Weber
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG)
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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 Recent advancements in video generation and editing have led to significant improvements in quality. However, most techniques focus on modifying appearance rather than motion. Our paper proposes a novel approach that addresses this limitation by specifying motions using a single reference video. We also introduce the concept of using pre-trained image-to-video models instead of text-to-video models. This allows us to preserve the exact appearance and position of objects or scenes while disentangling appearance from motion. Our method, called motion-textual inversion, leverages the observation that image-to-video models primarily extract appearance from input images, while text/image embeddings control motion. We represent motion using text/image embedding tokens and operate on an inflated motion-text embedding to achieve high temporal granularity. Once optimized on the reference video, this embedding can be applied to various target images to generate videos with semantically similar motions. Our approach does not require spatial alignment between the reference video and target image, generalizes across domains, and can be applied to tasks such as reenactment, object motion control, and camera control. We empirically demonstrate the effectiveness of our method in semantic video motion transfer, outperforming existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making videos more realistic by changing how objects move. Right now, most video editing techniques just change what things look like, but they don’t do much with the way things move. Our new approach uses a special kind of model that can understand both pictures and videos to create more realistic motion in our edited videos. We tested this method and found it works really well, especially when we’re trying to make someone or something reenact a specific action. |
Keywords
» Artificial intelligence » Alignment » Embedding
