Summary of Avid: Adapting Video Diffusion Models to World Models, by Marc Rigter et al.
AVID: Adapting Video Diffusion Models to World Models
by Marc Rigter, Tarun Gupta, Agrin Hilmkil, Chao Ma
First submitted to arxiv on: 1 Oct 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 Large-scale generative models have achieved remarkable success in various domains. However, scaling-up foundation models for decision-making remains a challenge when action-labelled data is scarce, as seen in robotics and other sequential decision-making problems. To overcome this issue, researchers propose leveraging widely-available unlabelled videos to train world models that simulate the consequences of actions. These accurate world models can then be used to optimize decision-making in downstream tasks. Building upon existing image-to-video diffusion models, which generate highly realistic synthetic videos, a new approach adapts these models to action-conditioned world models without requiring access to the original model parameters. The proposed method, AVID, trains an adapter on a small domain-specific dataset of action-labelled videos and uses a learned mask to modify intermediate outputs and generate accurate action-conditioned videos. Experimental results demonstrate that AVID outperforms existing baselines for diffusion model adaptation on video game and real-world robotics data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large machines can learn from many things, like pictures and videos. But when these machines need to make decisions in a sequence, like a robot moving around, it’s hard to get them to work well without a lot of practice data. One idea is to use lots of unpracticed videos to teach the machine what might happen if it does something. This could help the machine make better choices later on. Some smart people have already made machines that can create fake videos that look very real. Now, they want to adapt these machines so they can make decisions based on actions, like a robot moving its arm up or down. They’re trying a new way of teaching the machine using some examples and then modifying what it learns to get better results. It seems to work pretty well! |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Mask
