This package is an implementation of the algorithm developed in the following paper: Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video. X. Zhou, M. Zhu, S. Leonardos, K. Derpanis, K. Daniilidis. CVPR 2016. How to use? - first run stratup.m - see demoH36M for an example from Human3.6M dataset - see demoHG for an example of how to use our algorithm combined with the "Stacked hourglass network" - see demoMPII for an example of how to reconstruct 3D poses from a single image from MPII dataset Notes: - the code for hourglass network in pose-hg-demo is from Newell et al., https://github.com/anewell/pose-hg-demo - see the comments in demoHG.m for how to run hourglass network on your images and save heatmaps - if you want to use the hourglass network, you need to first install Torch and make it work - generally "Hourglass network" + "poseDict-all-K128" (pose dictionary learned from Human3.6M) work well - for better 3D reconstruction, you can learn a 3D pose dictionary using your own mocap data - for more details on pose dictionary learning, please see the following project: http://cis.upenn.edu/~xiaowz/shapeconvex.html If you used this package in your work, please cite the following papers: @inproceedings{zhou2016sparseness, title={Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video}, author={Zhou, Xiaowei and Zhu, Menglong and Derpanis, Kosta and Daniilidis, Kostas}, booktitle={CVPR}, year={2016} } @article{zhou2016sparse, title={Sparse Representation for 3D Shape Estimation: A Convex Relaxation Approach}, author={Zhou, Xiaowei and Zhu, Menglong and Leonardos, Spyridon and Daniilidis, Kostas}, journal={arXiv preprint arXiv:1509.04309}, year={2016} }
chuxiaoselena/SparsenessMeetsDeepness
This is the code of "Human Pose Estimation from Monocular Video"
Matlab