Summary of Transformer-based Neuro-animator For Qualitative Simulation Of Soft Body Movement, by Somnuk Phon-amnuaisuk
Transformer-based Neuro-Animator for Qualitative Simulation of Soft Body Movement
by Somnuk Phon-Amnuaisuk
First submitted to arxiv on: 10 Aug 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 This paper explores the application of recent transformer architectures as a neuro-animator model, inspired by humans’ ability to qualitatively visualize and describe dynamic events from past experiences. The visual transformer model is trained to predict flag motions at the t+1 time step, given information of previous motions from t-n to t time steps. The results show that the architecture successfully learns temporal embedding of flag motions and produces reasonable quality simulations of flag waving under different wind forces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how our brains can predict what will happen in a physical situation without fully understanding the physics behind it. For example, if you see a flag waving in the wind, your brain can imagine how the flag might move next even though you don’t know the exact science of fluid dynamics. The researchers are trying to create a computer model that can do something similar, using a type of AI called transformers. They trained this model on videos of flags waving and found that it could accurately predict what would happen next. |
Keywords
» Artificial intelligence » Embedding » Transformer
