The testing code for OriNet
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src Update test.lua Aug 22, 2018
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README.md

README.md

OriNet-demo

This is the testing code for OriNet on MPI-INF-3DHP test set. Traing code will be released later.

The model was first pretrained on MPII and Human3.6M dataset and then fine-tuned on MPI-INF-3DHP training set without using any background augmentation.

The pretrained models can be downloaded here:

Dependencies:

Testing

  • Download the MPI-INF-3DHP dataset and annotation file and put the h5 file in data/mpi/
  • Suppose that the test sequences are located in mpi-inf-3dhp/test/
  • In directory src, run th test.lua -dataDir /path/to/mpi-inf-3dhp -loadModel /path/to/your/model to save the results in Result.txt
  • Go to directorytest_util and run evaluate.m in matlab to get the evaluation results. Remember to change the path to your annotation files. (The evaluation codes are provided in MPI-INF-3DHP dataset)

Results on MPI-INF-3DHP test set by activities

(Update) There has been a minor correction to the annotations for TS3 and TS4 in the test set. You should get the updated results based on this codebase.

Stand/ Walk Exercies Sit on Chair Crouch/ Reach On the Floor Sports Misc. All PCK All AUC MPJPE(mm)
Ours 95.5 82.3 89.9 84.6 66.5 92.0 93.0 84.3 47.5 84.5

Previous results ( for comparasion with published works)

Stand/ Walk Exercies Sit on Chair Crouch/ Reach On the Floor Sports Misc. All PCK All AUC MPJPE(mm)
VNect 87.7 77.4 74.7 72.9 51.3 83.3 80.1 76.7 40.4 124.7
Meta 86.6 75.3 74.8 73.7 52.2 82.1 77.5 75.7 39.3 117.6
Ours 90.4 79.1 88.5 81.6 66.3 91.9 92.2 81.8 45.2 89.4

Acknowledgements

We thank Alejandro Newell et al. for their great work and repo.

Reference

If you find our work useful in your research, please consider citing our paper:

@inproceedings{luo2018orinet,
  title     = {OriNet: A Fully Convolutional Network for 3D Human Pose Estimation}
  author    = {Luo, Chenxu and Chu, Xiao and Yuille, Alan}
  booktitle = {BMVC}
  year      = {2018}
}