Summary of Uncertainty-aware Testing-time Optimization For 3d Human Pose Estimation, by Ti Wang et al.
Uncertainty-Aware Testing-Time Optimization for 3D Human Pose Estimation
by Ti Wang, Mengyuan Liu, Hong Liu, Bin Ren, Yingxuan You, Wenhao Li, Nicu Sebe, Xia Li
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 UAO framework is an optimization-based method for 3D human pose estimation that addresses domain gaps and generalization issues. By quantifying the uncertainty of each joint, the approach alleviates overfitting problems while fine-tuning pre-trained models. The framework consists of a 2D-to-3D network for training and a projection loss for testing-time optimization. Experimental results on Human3.6M and MPI-INF-3DHP datasets show that UAO outperforms previous best results by a significant margin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new method helps computers better understand how humans move. Right now, computers can do a good job of predicting how people will pose their bodies in 2D pictures, but they struggle to generalize to 3D images. The new approach uses something called “uncertainty” to help the computer learn more and avoid mistakes. This means that if the computer is unsure about a particular joint movement, it won’t try to fit the data too tightly. Instead, it will adapt to new situations better. The result is more accurate predictions of human pose in 3D images. |
Keywords
* Artificial intelligence * Fine tuning * Generalization * Optimization * Overfitting * Pose estimation
