Summary of Rmd: a Simple Baseline For More General Human Motion Generation Via Training-free Retrieval-augmented Motion Diffuse, by Zhouyingcheng Liao et al.
RMD: A Simple Baseline for More General Human Motion Generation via Training-free Retrieval-Augmented Motion Diffuse
by Zhouyingcheng Liao, Mingyuan Zhang, Wenjia Wang, Lei Yang, Taku Komura
First submitted to arxiv on: 5 Dec 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 proposes a simple and effective baseline, RMD, for enhancing the generalization of motion generation in out-of-distribution scenarios. By leveraging retrieval-augmented techniques, RMD offers three key advantages: flexibly replacing the external database, reusing body parts from the motion database with the help of a language model, and utilizing a pre-trained motion diffusion model as a prior to improve motion quality. Without any training, RMD achieves state-of-the-art performance on out-of-distribution data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to make motion generation better at handling unexpected situations. They call it RMD and it’s really good because it can be used with different databases, use parts of existing motions, and even improve the quality of the generated motions. It doesn’t need any special training and does better than other methods on tricky data. |
Keywords
» Artificial intelligence » Diffusion model » Generalization » Language model
