GNet-pose

Project Page: http://guanghan.info/projects/guided-fractal/

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Overview

Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation.

Source code release of the paper for reproduction of experimental results, and to aid researchers in future research.

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Prerequisites

• Python 2.7 or Python 3.3+
• Modified Caffe

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Getting Started

1. Download Data and Pre-trained Models

• Datasets (MPII [1], LSP [2])

  bash ./get_dataset.sh
  

• Models

  bash ./get_models.sh
  

• Predictions (optional)

  bash ./get_preds.sh
  

2. Testing

• Generate cropped patches from the dataset for testing:

  cd testing/
  matlab gen_cropped_LSP_test_images.m
  matlab gen_cropped_MPII_test_images.m
  cd -
  

  This will generate images with 368-by-368 resolution.

• Reproduce the results with the pre-trained model:

  cd testing/
  python .test.py
  cd -
  

  You can choose different dataset to test on, with different models. You can also choose different settings in test.py, e.g., with or without flipping, scaling, cross-heatmap regression, etc.

3. Training

• Generate Annotations

  cd training/Annotations/
  matlab MPI.m LEEDS.m
  cd -
  

  This will generate annotations in json files.

• Generate LMDB

  python ./training/Data/genLMDB.py
  

  This will load images from dataset and annotations from json files, and generate lmdb files for caffe training.

• Generate Prototxt files (optional)

  python ./training/GNet/scripts/gen_GNet.py
  python ./training/GNet/scripts/gen_fractal.py
  python ./training/GNet/scripts/gen_hourglass.py
  

• Training:

   bash ./training/train.sh
  

4. Performance Evaluation

cd testing/eval_LSP/; matlab test_evaluation_lsp.m; cd../

cd testing/eval_MPII/; matlab test_evaluation_mpii_test.m

5. Results

More Qualitative results can be found in the project page. Quantitative results please refer to the arxiv paper.

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License

GNet-pose is released under the Apache License Version 2.0 (refer to the LICENSE file for details).

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Citation

The details are published as a technical report on arXiv. If you use the code and models, please cite the following paper: arXiv:1705.02407.

@article{ning2017knowledge,
  title={Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation},
  author={Ning, Guanghan and Zhang, Zhi and He, Zhihai},
  journal={arXiv preprint arXiv:1705.02407},
  year={2017}
}

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Reference

[1] Andriluka M, Pishchulin L, Gehler P, et al. "2d human pose estimation: New benchmark and state of the art analysis." CVPR (2014).

[2] Sam Johnson and Mark Everingham. "Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation." BMVC (2010).