Summary of Thermohands: a Benchmark For 3d Hand Pose Estimation From Egocentric Thermal Images, by Fangqiang Ding et al.
ThermoHands: A Benchmark for 3D Hand Pose Estimation from Egocentric Thermal Images
by Fangqiang Ding, Yunzhou Zhu, Xiangyu Wen, Gaowen Liu, Chris Xiaoxuan Lu
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); 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 proposed ThermoHands benchmark addresses the limitations of previous RGB or NIR-based approaches for egocentric 3D hand pose estimation. The benchmark utilizes thermal image-based methods, such as TherFormer, a new baseline method utilizing dual transformer modules. The results demonstrate TherFormer’s leading performance and affirm thermal imaging’s effectiveness in enabling robust 3D hand pose estimation under adverse conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Thermal images can help with hand pose estimation. A new system called ThermoHands helps estimate the position of hands in different scenarios. It uses thermal images, which are not affected by lighting or obstructions like handwear. The system has a database of 28 people performing tasks and accurately annotates their hand positions. The results show that this approach is better than previous methods and can help with applications like virtual reality. |
Keywords
* Artificial intelligence * Pose estimation * Transformer
