Summary of Enhancing 3d Human Pose Estimation Amidst Severe Occlusion with Dual Transformer Fusion, by Mehwish Ghafoor et al.
Enhancing 3D Human Pose Estimation Amidst Severe Occlusion with Dual Transformer Fusion
by Mehwish Ghafoor, Arif Mahmood, Muhammad Bilal
First submitted to arxiv on: 6 Oct 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 This paper introduces a novel approach to 3D Human Pose Estimation from monocular videos, called Dual Transformer Fusion (DTF). The DTF algorithm leverages spatial and temporal cues to infer 3D poses from 2D joint observations, even in the presence of severe occlusions. The proposed method consists of a temporal interpolation-based occlusion guidance mechanism to address missing joint data, followed by a self-refinement schema for intermediate-view refinement and final 3D pose estimation. The entire system is end-to-end trainable. Experiments on the Human3.6M and MPI-INF-3DHP datasets demonstrate that DTF outperforms existing state-of-the-art methods, achieving substantial improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to estimate human poses from videos. It’s like trying to figure out where someone’s joints are based only on what they look like in a 2D picture. The problem is that sometimes parts of the body get hidden or obscured, making it harder to do this task accurately. The researchers came up with an innovative approach called Dual Transformer Fusion (DTF) to solve this issue. They used special techniques to fill in the missing information and then refined their results using a self-correcting process. This method was tested on two large datasets and showed significant improvements over other methods. |
Keywords
» Artificial intelligence » Pose estimation » Transformer
