Summary of Scamo: Exploring the Scaling Law in Autoregressive Motion Generation Model, by Shunlin Lu et al.
ScaMo: Exploring the Scaling Law in Autoregressive Motion Generation Model
by Shunlin Lu, Jingbo Wang, Zeyu Lu, Ling-Hao Chen, Wenxun Dai, Junting Dong, Zhiyang Dou, Bo Dai, Ruimao Zhang
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The paper introduces a scalable motion generation framework that includes Motion FSQ-VAE and a text-prefix autoregressive transformer. The authors validate the scaling law in the context of motion generation by demonstrating the logarithmic relationship between normalized test loss and compute budgets, as well as the power law between Non-Vocabulary Parameters, Vocabulary Parameters, and Data Tokens. Leveraging this scaling law, they predict optimal model size, vocabulary size, and data requirements for a given compute budget, which aligns with the predicted test loss when trained with these optimal parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to make machines create movements that are like real-life actions. They use special tools called Motion FSQ-VAE and text-prefix autoregressive transformers to do this. The authors tested their system and found a pattern, or “scaling law,” that helps them predict what’s needed for the machine to work well. This means they can figure out how big the model should be, how many words it needs to learn, and how much data it requires to create good movements. |
Keywords
» Artificial intelligence » Autoregressive » Transformer
