Paper (oral) GCPR 2019: https://arxiv.org/pdf/1912.06274.pdf

supp. material: http://pages.iai.uni-bonn.de/gall_juergen/download/kp-vpi_supp.pdf

Models Keypoint + Viewpont Estimation (Caffe):

• ObjectNet3D - KP+VP Real Data

https://drive.google.com/file/d/1ZdF6RZUkUrtf7yfvnt8L-_CmK58nyvv7/view?usp=sharing

• ObjectNet3D - KP+VP Real + Synthetic (ShapeNet+Own) Data

https://drive.google.com/file/d/11Njc2Z8W3jzQg8428LXj1unIjLFtfFOZ/view?usp=sharing

Models Viewpoint Estimation Only (Caffe):

• ObjectNet3D - Classication Real Data

https://drive.google.com/file/d/1BdUan6iMjUm-o4m8gLKK71RWwNTPYJMy/view?usp=sharing

• ObjectNet3D - Classification Real + Synthetic (ShapeNet+Own) Data

https://drive.google.com/file/d/1WrJzm5MWVpdvVgutbQ-zOMrWa2TJys5W/view?usp=sharing

• Pascal3D - Classication Real Data

https://drive.google.com/file/d/13iu9UOGRIEOtZPV3-RLHXGEHiFXlDgFA/view?usp=sharing

• Pascal3D - Classification Real + Synthetic (ShapeNet+Own) Data

https://drive.google.com/file/d/1VLKK_mRmVn3tYygJBwmBEikdpeaxeXUK/view?usp=sharing

• Caffe-git is an enhanced copy of Caffe version github on Dec 2016

Source: https://drive.google.com/file/d/1FjJcTY8x4lxyk_ErZIto9O52uaD62OsR/view?usp=sharing

• The given VS2013 should be working straight forward

Binaries: https://drive.google.com/file/d/1IcQJGt6U_fxJzkD2q2lm9bqzmPwP1nUy/view?usp=sharing

• caffe.mexw64 for matlab found here: ~/features/CNN/

• Matlab (tested on version 2013a)
• Cygwin terminal for running the trainings
• Python 2.7.1X

• Training: Create models to be trained (matlab):
  • have a look at main_pose_train.m, based on InputParameters.m, the core function
  • cpm = only keypoint estimation, vp = only viewpoint and cpm-vp = joint trainingTrain a model:
  • Open a cygwin (a cmd promp should work as well, although it might crashed right after the first training, in case you have a sequence of models to be trained in a .sh file. 
  • The generated models to be trained (solver.prototxt, traintest.prototxt will be called from "./train.sh".
• Test those models:
  • Follow Run_Pose and modify the 2 main functions: main_pose_test.m and poseEstimation.m if necessary. They are also based on InputParameters.m

• "synthetic" data means synthetic data generated from Render repository
• "shapenet" data means synthetic data from the shapenet repository: https://github.com/ShapeNet/RenderForCNN

• You can obtain the gt keypoints from Tulsiani et al. (ICCV 2015) from: http://www.cs.berkeley.edu/~shubhtuls/cachedir/vpsKps/segkps.zip
• Caffe pre-trained model weights can be downloaded here:
  • VGG (16 layers): http://www.robots.ox.ac.uk/~vgg/research/very_deep/
  • Human keypoint (pose) estimation: http://pearl.vasc.ri.cmu.edu/caffe_model_github/model/_trained_MPI/pose_iter_320000.caffemodel