Summary of Monkey See, Monkey Do: Harnessing Self-attention in Motion Diffusion For Zero-shot Motion Transfer, by Sigal Raab et al.
Monkey See, Monkey Do: Harnessing Self-attention in Motion Diffusion for Zero-shot Motion Transfer
by Sigal Raab, Inbar Gat, Nathan Sala, Guy Tevet, Rotem Shalev-Arkushin, Ohad Fried, Amit H. Bermano, Daniel Cohen-Or
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 explores the application of pre-trained motion diffusion models for motion editing, specifically focusing on leveraging the attention mechanism within these models. The authors propose a novel approach called MoMo (Monkey See, Monkey Do), which enables zero-shot motion transfer by manipulating the latent feature space. This technique allows for tasks such as synthesizing out-of-distribution motions, style transfer, and spatial editing. Unlike existing methods that require training for specific applications, MoMo operates at inference time, requiring no additional training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computers to edit movements like dancing or walking. Right now, these computers are really good at making new movements, but they’re not very good at changing the way a movement looks or feels. The people who did this research wanted to make it so that these computers could do more things with movements, like make them look or feel different without needing to learn all over again. They came up with a new idea called MoMo (Monkey See, Monkey Do), which is like how monkeys imitate each other’s movements. This new way of doing things lets the computers change movements in lots of ways, including making new ones that are very different from what they’ve seen before. |
Keywords
» Artificial intelligence » Attention » Diffusion » Inference » Style transfer » Zero shot
