Summary of Owl-1: Omni World Model For Consistent Long Video Generation, by Yuanhui Huang et al.
Owl-1: Omni World Model for Consistent Long Video Generation
by Yuanhui Huang, Wenzhao Zheng, Yuan Gao, Xin Tao, Pengfei Wan, Di Zhang, Jie Zhou, Jiwen Lu
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 proposes a novel approach to long-term coherent and comprehensive video generation using Omni World model (Owl-1). Existing methods iterate VGMs, but this only captures short-term information, leading to inconsistent results. Owl-1 addresses this by modeling the underlying evolving world in a latent space and using VGMs to film it into videos. The approach enhances diversity and consistency through interaction between dynamics and persistent state. Experiments show comparable performance with SOTA methods on VBench-I2V and VBench-Long, validating high-quality video generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating really cool and realistic videos using a new way to make computer models work together. It’s like a big puzzle where each piece helps create a complete picture of the world. The old method just copied what was happening at that moment, but this new approach looks at how things change over time and makes more sense. |
Keywords
» Artificial intelligence » Latent space
