Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources

This code implements a demo of the Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources paper by Adrian Bulat and Georgios Tzimiropoulos.

[2021 Update]: PyTorch repo with training code for BNN available here: https://github.com/1adrianb/binary-networks-pytorch

For the Face Alignment demo please check: https://github.com/1adrianb/binary-face-alignment

Requirements

• Install the latest Torch7 version (for Windows, please follow the instructions avaialable here)

Packages

• cutorch
• nn
• cudnn (cudnn5 preffered)
• xlua
• image
• gnuplot
• cURL
• paths

Setup

Clone the github repository

git clone https://github.com/1adrianb/binary-human-pose-estimation --recursive
cd binary-human-pose-estimation

Build and install the BinaryConvolution package

cd bnn.torch/; luarocks make; cd ..;

Install the modified optnet package

cd optimize-net/; luarocks make rocks/optnet-scm-1.rockspec; cd ..;

Run the following command to prepare the files required by the demo. This will download 10 images from the MPII dataset alongside the dataset structure converted to .t7

th download-content.lua

Download the model available bellow and place it in the models folder.

Usage

In order to run the demo simply type:

th main.lua

Pretrained models

Layer type	Model Size	MPII error
MPII	1.3MB	76.0

Note: More pretrained models will be added soon

Notes

For more details/questions please visit the project page or send an email at adrian.bulat@nottingham.ac.uk