Summary of Physgen: Rigid-body Physics-grounded Image-to-video Generation, by Shaowei Liu et al.
PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation
by Shaowei Liu, Zhongzheng Ren, Saurabh Gupta, Shenlong Wang
First submitted to arxiv on: 27 Sep 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 PhysGen method converts a single image and an input condition into a realistic, physically plausible, and temporally consistent video. The approach integrates model-based physical simulation with data-driven video generation, enabling plausible image-space dynamics. Key components include image understanding, image-space dynamics simulation, and image-based rendering and refinement. The resulting videos are realistic in both physics and appearance and can be used for applications such as turning an image into a realistic animation or allowing users to interact with the image. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PhysGen is a new way to turn images into videos that look real and make sense physically. It works by using computer simulations of how objects move and behave, along with special algorithms that create realistic video footage. This means you can take an image and turn it into a moving animation or even let users interact with the image in fun ways. |
