Summary of World-grounded Human Motion Recovery Via Gravity-view Coordinates, by Zehong Shen et al.
World-Grounded Human Motion Recovery via Gravity-View Coordinates
by Zehong Shen, Huaijin Pi, Yan Xia, Zhi Cen, Sida Peng, Zechen Hu, Hujun Bao, Ruizhen Hu, Xiaowei Zhou
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel Gravity-View (GV) method for recovering world-grounded human motion from monocular video proposes estimating human poses in a GV coordinate system, defined by the world gravity and camera view direction. This approach eliminates ambiguity in previous autoregressive methods that predict relative motion, reducing error accumulation. The estimated poses are then transformed back to the world coordinate system using camera rotations, forming a global motion sequence. Experimental results on in-the-wild benchmarks show improved accuracy and speed compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand how people move by watching videos from just one camera. They came up with a new way to figure out where the person is moving in the world, not just in relation to the camera. This makes their results more accurate and useful for things like tracking athletes or monitoring patients. The code for this project is available online. |
Keywords
» Artificial intelligence » Autoregressive » Tracking
