Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi. CVPR, 2020 (Oral presentation).
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 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
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>
Coming soon.
Parts of the code are based on CycleGAN.