Summary of Videoagent: Self-improving Video Generation, by Achint Soni et al.
VideoAgent: Self-Improving Video Generation
by Achint Soni, Sreyas Venkataraman, Abhranil Chandra, Sebastian Fischmeister, Percy Liang, Bo Dai, Sherry Yang
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 Video generation has been used to generate visual plans for controlling robotic systems, but a major bottleneck in leveraging video generation for control lies in the quality of the generated videos, which often suffer from hallucinatory content and unrealistic physics. While scaling up dataset and model size provides a partial solution, integrating external feedback is both natural and essential for grounding video generation in the real world. The proposed VideoAgent uses self-conditioning consistency to refine generated video plans based on external feedback, allowing inference-time compute to be turned into better generated video plans. As the refined video plan is being executed, VideoAgent can collect additional data from the environment to further improve video plan generation. Experiments show that VideoAgent drastically reduces hallucination, thereby boosting success rate of downstream manipulation tasks. It also effectively refines real-robot videos, providing an early indicator that robots can be an effective tool in grounding video generation in the physical world. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video generation is used to create plans for controlling robotic systems, but there’s a big problem: the generated videos are often fake and unrealistic! To make things better, scientists proposed a new way called VideoAgent. It takes the generated videos and makes them more realistic by adding feedback from the environment. This helps the robots perform tasks better and even improves over time. The results show that VideoAgent is very good at making videos that are not fake and allows robots to do tasks successfully. |
Keywords
» Artificial intelligence » Boosting » Grounding » Hallucination » Inference
