Summary of Using Motion Cues to Supervise Single-frame Body Pose and Shape Estimation in Low Data Regimes, by Andrey Davydov et al.
Using Motion Cues to Supervise Single-Frame Body Pose and Shape Estimation in Low Data Regimes
by Andrey Davydov, Alexey Sidnev, Artsiom Sanakoyeu, Yuhua Chen, Mathieu Salzmann, Pascal Fua
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper presents a method for estimating human body pose and shape using deep-learning algorithms, even when there is limited annotated training data available. By incorporating priors from databases of body shapes or leveraging unannotated videos as supervision signals, the approach can mitigate the effects of too little annotated data. The authors demonstrate that by enforcing consistency between the image optical flow and the change in pose between consecutive frames, a trained model can be refined to perform on par with methods using more annotated data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to estimate human body shape and pose even when there is not much training data available. Usually, this requires lots of labeled pictures or videos, but that’s not always possible. The authors found a way to use unannotated videos as extra help for the model. They do this by comparing consecutive frames in a video and making sure they match what the model thinks should happen based on how people move. This makes the model better and lets it perform just as well as ones trained with lots more data. |
Keywords
* Artificial intelligence * Deep learning * Optical flow
