Summary of Most: Motion Style Transformer Between Diverse Action Contents, by Boeun Kim et al.
MoST: Motion Style Transformer between Diverse Action Contents
by Boeun Kim, Jungho Kim, Hyung Jin Chang, Jin Young Choi
First submitted to arxiv on: 10 Mar 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 motion style transformer effectively disentangles style from content, generating plausible motions with transferred styles. By introducing a novel architecture with part-attentive style modulators and Siamese encoders that separately encode style and content features, the method outperforms existing approaches in transferring style between motions with different contents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at copying the way people move, like dancing or playing sports. Currently, computers can only copy movements if they’re doing the same thing as before. But what if someone wants to make a robot do a new dance? This paper solves that problem by creating a special computer program that can take one movement and change it into another. The program is really good at doing this, even when the two movements are very different. |
Keywords
» Artificial intelligence » Transformer
