Using k-poselets for detecting people and localizing their keypoints

June, 2014


A k-poselet is a deformable part model (DPM) with k parts, where each of the parts is a poselet, aligned to a specific configuration of keypoints based on ground-truth annotations. A separate template is used to learn the appearance of each part. The parts are allowed to move with respect to each other with a deformation cost that is learned at training time. This model is richer than both the traditional version of poselets and DPMs. It enables a unified approach to person detection and keypoint prediction which, barring contemporaneous approaches based on CNN features, achieves state-of-the-art keypoint prediction while maintaining competitive detection performance



You can download a complete implementation of k-poselets as described in the paper, along with demo code and pretrained models.
Source code and k-poselet models download: kposelets-release.tar.gz

The system is implemented in MATLAB, with helper functions written in C/C++ for efficiency reasons.

We also maintain a repository on github.


You can download the dataset, as used in the paper.

Dataset download: dataset.tar.gz

How to cite

When citing our system, please cite this work. The bibtex entry is provided below for your convenience.

 Author = {G. Gkioxari and B. Hariharan and R. Girshick and J. Malik},
 Title = {Using k-poselets for detecting people and localizing their keypoints},
 Booktitle = {CVPR},
 Year = {2014}}


For any questions regarding the work or the implementation, contact the authors at and