Summary of Occlusion Resilient 3d Human Pose Estimation, by Soumava Kumar Roy et al.
Occlusion Resilient 3D Human Pose Estimation
by Soumava Kumar Roy, Ilia Badanin, Sina Honari, Pascal Fua
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 In this paper, researchers tackle a long-standing problem in computer vision: estimating human body poses from single-camera videos while accounting for occlusions. To address this challenge, they propose a novel approach that incorporates temporal consistency and explicit modeling of occlusions. The authors leverage 3D pose estimation models to predict the likelihood of occlusion and utilize this information to refine their predictions. This approach is evaluated on various benchmark datasets, demonstrating improved accuracy and robustness compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in making robots and computers understand how humans move. Right now, cameras can get confused when parts of the body are hidden or blocked. To fix this, scientists created a new way to predict where the body is moving while also figuring out when it’s being covered up. They tested their method on lots of videos and found that it worked better than other ways people have tried to solve this issue. |
Keywords
* Artificial intelligence * Likelihood * Pose estimation
