Summary of Addbiomechanics Dataset: Capturing the Physics Of Human Motion at Scale, by Keenon Werling et al.
AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale
by Keenon Werling, Janelle Kaneda, Alan Tan, Rishi Agarwal, Six Skov, Tom Van Wouwe, Scott Uhlrich, Nicholas Bianco, Carmichael Ong, Antoine Falisse, Shardul Sapkota, Aidan Chandra, Joshua Carter, Ezio Preatoni, Benjamin Fregly, Jennifer Hicks, Scott Delp, C. Karen Liu
First submitted to arxiv on: 16 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Robotics (cs.RO)
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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 dataset, AddBiomechanics Dataset 1.0, tackles the challenge of quantifying human motion dynamics by providing a comprehensive collection of pose and force data for over 70 hours of movement from 273 subjects. This medium-difficulty summary highlights the importance of this dataset in facilitating advancements in reconstructing human poses in 3D from inexpensive sensors. The researchers developed novel analytical methods to construct this dataset, which is publicly available at https://addbiomechanics.org/download_data.html. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The AddBiomechanics Dataset 1.0 is a big deal! It’s like having a super-powerful tool to help robots and computers understand how humans move. This means we can make better robots that can work with us, or even create new kinds of games and simulations that are super realistic. The dataset has lots of data from many people doing different movements, which is really important because it helps scientists figure out the best way to use this information. |
