Stacked Hourglass Networks for Human Pose Estimation

Alejandro Newell, Kaiyu Yang, Jia Deng


This work introduces a novel convolutional network architecture for the task of human pose estimation. Features are processed across all scales and consolidated to best capture the various spatial relationships associated with the body. We show how repeated bottom-up, top-down processing used in conjunction with intermediate supervision is critical to improving the performance of the network. We refer to the architecture as a "stacked hourglass" network based on the successive steps of pooling and upsampling that are done to produce a final set of predictions. State-of-the-art results are achieved on the FLIC and MPII benchmarks outcompeting all recent methods.

Alejandro Newell, Kaiyu Yang, and Jia Deng
Stacked Hourglass Networks for Human Pose Estimation.
European Conference on Computer Vision (ECCV), 2016 [link to paper]


Training code here.
Demo code here.
Pre-trained model here.


Please send any questions or comments to Alejandro Newell at