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LCR-Net: Real-time multi-person 2D and 3D human pose estimation

Grégory Rogez    Philippe Weinzaepfel    Cordelia Schmid

CVPR 2017   --   IEEE Trans. on PAMI 2019


We propose an end-to-end architecture for real-time 2D and 3D human pose estimation in natural images. Key to our approach is the generation and scoring of a number of pose proposals per image, which allows us to predict 2D and 3D pose of multiple people simultaneously. Hence, our approach does not require an approximate localization of the humans for initialization. Our architecture, named LCR-Net, contains 3 main components: 1) the pose proposal generator that suggests potential poses at different locations in the image; 2) a classifier that scores the different pose proposals ; and 3) a regressor that refines pose proposals both in 2D and 3D. All three stages share the convolutional feature layers and are trained jointly. The final pose estimation is obtained by integrating over neighboring pose hypotheses , which is shown to improve over a standard non maximum suppression algorithm. Our approach significantly outperforms the state of the art in 3D pose estimation on Human3.6M, a controlled environment. Moreover, it shows promising results on real images for both single and multi-person subsets of the MPII 2D pose benchmark.

Real-time Multi-person Human Pose Estimation (2D+3D) Demo @ CVPR'18 (v1.0) and ECCV'18 (v 2.0)


Pytorch code (v2.x) with ResNet backbone

This release is for scientific or personal use only. It includes code for testing existing models.
For commercial use and licensing of the training pipeline, contact us at:



To test a given model:

python <modelname> <imagename> <gpuid>

The list of available models are:

Example to test our model trained on Human 3.6M:

python Human3.6-17J-ResNet50 Directions1_S11_C1_1.jpg 0

Example to test our model for in the wild pose detection:

python InTheWild-ResNet50 058017637.jpg 0

Problem loading the pickle model
You may get errors when loading the pickle file containing the cnn weights, anchor poses and parameters. If this is the case, please replace L120-L131 of by the following line.
model = {} for suffix in ['_model.pth.tgz', '_ppi_params.pkl', '_anchor_poses.pkl', 'cfg.pkl']: fname = os.path.join( os.path.dirname(__file__), 'models', modelname+suffix) if not os.path.isfile(fname): # Download the files if not os.path.isdir(os.path.dirname(fname)): os.system('mkdir -p "{:s}"'.format(dirname)) os.system('wget{:s} -P {:s}'.format(modelname+suffix, dirname)) if not os.path.isfile(fname): raise Exception("ERROR: download incomplete") if fname.endswith('pkl'): with open(fname, 'rb') as fid: model[suffix[1:-4]] = pickle.load(fid) else: model['model'] = torch.load(fname)


If you use our new models, please cite our PAMI paper:

  TITLE = {{LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images}},
  AUTHOR = {Rogez, Gr\'egory and Weinzaepfel, Philippe and Schmid, Cordelia},
  JOURNAL = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}}, 
  YEAR = {2019},
  publisher = {IEEE},

Caffe code (v1.x) with VGG backbone

Please note that our code is released only for scientific or personal use.

We only provide code for testing our models, not for training.



To test our model trained on Human 3.6M:

python h36m_100_p2 Directions1_S11_C1_1.jpg 0

To test our model for in the wild pose detection:

python mix_200x2 058017637.jpg 0

The arguments are:


If you use our code, please cite our CVPR'17 paper:

  TITLE = {{LCR-Net: Localization-Classification-Regression for Human Pose}},
  AUTHOR = {Rogez, Gregory and Weinzaepfel, Philippe and Schmid, Cordelia},
  ADDRESS = {Honolulu, United States},
  YEAR = {2017},
  MONTH = July,