| # DeepPose: Human pose estimation via deep neural networks | |
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| <details> | |
| <summary align="right"><a href="http://openaccess.thecvf.com/content_cvpr_2014/html/Toshev_DeepPose_Human_Pose_2014_CVPR_paper.html">DeepPose (CVPR'2014)</a></summary> | |
| ```bibtex | |
| @inproceedings{toshev2014deeppose, | |
| title={Deeppose: Human pose estimation via deep neural networks}, | |
| author={Toshev, Alexander and Szegedy, Christian}, | |
| booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, | |
| pages={1653--1660}, | |
| year={2014} | |
| } | |
| ``` | |
| </details> | |
| ## Abstract | |
| <!-- [ABSTRACT] --> | |
| We propose a method for human pose estimation based on Deep Neural Networks (DNNs). The pose estimation is formulated as a DNN-based regression problem towards body joints. We present a cascade of such DNN regressors which results in high precision pose estimates. The approach has the advantage of reasoning about pose in a holistic fashion and has a simple but yet powerful formulation which capitalizes on recent advances in Deep Learning. We present a detailed empirical analysis with state-of-art or better performance on four academic benchmarks of diverse real-world images. | |
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| <div align=center> | |
| <img src="https://user-images.githubusercontent.com/15977946/146515040-a82a8a29-d6bc-42f1-a2ab-7dfa610ce363.png"> | |
| </div> | |