Summary of Estimating Body and Hand Motion in An Ego-sensed World, by Brent Yi et al.
Estimating Body and Hand Motion in an Ego-sensed World
by Brent Yi, Vickie Ye, Maya Zheng, Yunqi Li, Lea Müller, Georgios Pavlakos, Yi Ma, Jitendra Malik, Angjoo Kanazawa
First submitted to arxiv on: 4 Oct 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 A novel system called EgoAllo enables human motion estimation from a head-mounted device, leveraging egocentric SLAM poses and images to predict 3D body pose, height, and hand parameters. This is achieved by guiding sampling from a conditional diffusion model. The system’s key innovation lies in its representation, which includes spatial and temporal invariance criteria to boost performance. A head motion conditioning parameterization improves estimation accuracy by up to 18%. Furthermore, the estimated bodies can enhance hand estimation, reducing world-frame errors in single-frame estimates by 40%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EgoAllo is a system that helps us understand how people move when wearing a special device on their head. It uses information from the device and the scene around them to figure out what’s happening with their body and hands. This is important because it can help us improve our understanding of human motion, which has many applications like virtual reality or robotics. |
Keywords
» Artificial intelligence » Diffusion model
