Loading and Visualizing MPII Human Pose dataset in Python.
The Jupyter notebook mpii_visualization.ipynb in the notebooks folder demostrates reading and interpreting the MPII human pose dataset.
Sample output visualizations are available in results/viz_gt.
Before executing the notebook, download and paste all the MPII images *.jpg from http://human-pose.mpi-inf.mpg.de/#downloadin the directory data/mpii/images/*.jpg.
Important!: This remains my most cloned repository, so if you find it useful please do consider starring!
Please feel free to browse through my latest work on Active Learning for Human Pose Estimation:
VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation
Bayesian Uncertainty and Expected Gradient Length - Regression: Two Sides of the Same Coin?
A Mathematical Analysis of Learning Loss for Active Learning in Regression
If you found this code useful, please consider citing all three publications (it doesn't cost anything) :D Also, feedback is welcome!
Please contact me at megh.shukla [at] epfl.ch
@inproceedings{Shukla_2022_BMVC,
author = {Megh Shukla and Roshan Roy and Pankaj Singh and Shuaib Ahmed and Alexandre Alahi},
title = {VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0610.pdf}
}
@INPROCEEDINGS{9706805,
author={Shukla, Megh},
booktitle={2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
title={Bayesian Uncertainty and Expected Gradient Length - Regression: Two Sides Of The Same Coin?},
year={2022},
volume={},
number={},
pages={2021-2030},
doi={10.1109/WACV51458.2022.00208}}
@INPROCEEDINGS{9523037,
author={Shukla, Megh and Ahmed, Shuaib},
booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
title={A Mathematical Analysis of Learning Loss for Active Learning in Regression},
year={2021},
volume={},
number={},
pages={3315-3323},
doi={10.1109/CVPRW53098.2021.00370}}