Summary of Quo Vadis, Motion Generation? From Large Language Models to Large Motion Models, by Ye Wang et al.
Quo Vadis, Motion Generation? From Large Language Models to Large Motion Models
by Ye Wang, Sipeng Zheng, Bin Cao, Qianshan Wei, Qin Jin, Zongqing Lu
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 a new benchmark, MotionBase, which offers a million-level dataset for human motion understanding. This dataset features multimodal data with detailed text descriptions and is 15 times larger than previous largest datasets. The authors use this dataset to train a large motion model that performs well across various motions, including unseen ones. They also investigate the importance of scaling both data and model size, highlighting the role of synthetic data and pseudo labels in reducing costs. Additionally, they introduce a novel approach for motion tokenization that preserves motion information and expands codebook capacity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a big dataset called MotionBase to help machines understand human movements better. It has lots of examples with text descriptions and is really big! They use this data to train a model that can recognize different motions, even new ones it hasn’t seen before. The authors also talk about how important it is to have enough data and a strong model to make good predictions. Plus, they come up with a new way to describe movements so machines can understand them better. |
Keywords
» Artificial intelligence » Synthetic data » Tokenization
