This is the authors' implementation of Unsupervised Adversarial Learning of 3D Human Pose from 2D Joint Locations

1. Download openpose pretrained model
   • openpose_pose_coco.prototxt
     • https://github.com/opencv/opencv_extra/blob/3.4.1/testdata/dnn/openpose_pose_coco.prototxt
   • pose_iter_440000.caffemodel
     • http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_iter_440000.caffemodel
2. Run Inference
   • python bin/demo.py sample/image.png --lift_model sample/gen_epoch_500.npz --model2d pose_iter_440000.caffemodel --proto2d openpose_pose_coco.prototxt
   • Need OpenCV >= 3.4
     • < 3.3 results extreamly wrong estimation

• Python 3.6.5
• Cupy 4.0.0
• Chainer 4.0.0
• OpenCV 3.4 (when showing results)
• git-lfs
  • to download pre-trained model
  • or you can download pre-trained model directory from https://github.com/DwangoMediaVillage/3dpose_gan/blob/master/sample/gen_epoch_500.npz?raw=true

• Unsupervised learning of 3D points from ground truth 2D points

  python bin/train.py --gpu 0 --mode unsupervised --dataset h36m --use_heuristic_loss
  

• Unsupervised learning of 3D points from detected 2D points by Stacked Hourglass

  TBA

• Supervised learning of 3D points from ground truth 2D points

  python bin/train.py --gpu 0 --mode supervised --activate_func relu --use_bn
  

TBA

TBA

python bin/eval.py results/hoge/gen_epoch_*.npz