jingmingshi / keypointgan

KeypointGAN: Self-supervised Learning of Interpretable Keypoints from Unlabelled Videos

Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi. CVPR, 2020 (Oral presentation).

Quick start

Download Simplified Human3.6M dataset from http://fy.z-yt.net/files.ytzhang.net/lmdis-rep/release-v1/human3.6m/human_images.tar.gz into ./datasets/simple_human36m/human_images.

Download a network for perceptual loss from http://www.robots.ox.ac.uk/~vgg/research/unsupervised_pose/resources/imagenet-vgg-verydeep-19.mat into ./networks/imagenet-vgg-verydeep-19.mat.

Paths to datasets and checkpoints can be also customized in configs/simple_human36m.yaml and configs/test_simple_human.yaml

Training

Training requires pre-trained keypoint regressor. See bellow for instructions on how to do the pre-training.

A pre-trained regressor can be also downloaded from http://www.robots.ox.ac.uk/~vgg/research/unsupervised_pose/resources/simple_human36m_regressor/580000_net_regressor.pth. Save the regressor into ./checkpoints/simple_human36m_regressor directory unless you specified a different path in the config above.

Train a model on Simplified Human3.6M dataset

python2.7 train.py -c configs/simple_human36m.yaml

Testing

Test a model on Simplified Human3.6M dataset

python2.7 test_pose.py --test_config configs/test_simple_human.yaml -c configs/simple_human36m.yaml --iteration <ITERATION_NUMBER>

Pre-training regressor

Coming soon.

Acknowledgments

Parts of the code are based on CycleGAN.