Summary of Improving Dynamic Object Interactions in Text-to-video Generation with Ai Feedback, by Hiroki Furuta et al.
Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback
by Hiroki Furuta, Heiga Zen, Dale Schuurmans, Aleksandra Faust, Yutaka Matsuo, Percy Liang, Sherry Yang
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 explores ways to improve the object dynamics in large text-to-video models, which struggle to depict realistic movements. The authors propose aligning generated outputs with desired outcomes using external feedback, enabling autonomous refinement without manual data collection. They investigate the types of feedback and self-improvement algorithms that can enhance text-video alignment and realistic object interactions. The authors derive a unified probabilistic objective for offline RL finetuning and optimize text-video alignment metrics like CLIP scores and optical flow. However, these methods often fail to align with human perceptions of generation quality. To address this limitation, the authors propose using vision-language models to provide nuanced feedback tailored to object dynamics in videos. The experiments demonstrate that the method can effectively optimize rewards, with AI feedback driving significant improvements in video quality for dynamic interactions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make large text-to-video models better at showing how objects move and interact. Right now, these models don’t do a great job of this because they often show unrealistic movements. The authors think that if we give the model feedback on what it got wrong, it can learn to do better without us having to collect lots of data. They want to know what kind of feedback and learning methods work best for making the videos look more realistic. They come up with a new way of thinking about this problem and use it to optimize some metrics that measure how good the videos are. However, they find that these metrics don’t always match what humans think is good or bad. To fix this, they suggest using special models that can understand both images and text to give the model more helpful feedback. |
Keywords
» Artificial intelligence » Alignment » Optical flow
