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End-to-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human Pose Estimation

Pose estimation results on the LSP [1] dataset, the FLIC [2] dataset, and the Image Parse [3] dataset for the following paper.

Wei Yang, Wanli Ouyang, Hongsheng Li, Xiaogang Wang. "End-to-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human Pose Estimation". In CVPR, 2016.

Instruction

Please run demo_eval_DATASETNAME.m to evaluate a specific dataset. DATASETNAME can be lsp, flic or parse.

Acknowledgement

The evaluation code for the PCP and the PCK measurements are from a widely used version from the MPII Human Pose Dataset. The code for the PDJ measurement is from Chen and Yuille, NIPS'14

Citation

@InProceedings{yang2016end,
  Title 		= {End-to-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human Pose Estimation},
  Author 		= {Yang, Wei and Ouyang, Wanli and Li, Hongsheng and Wang, Xiaogang},
  Booktitle 	= {CVPR},
  Year 			= {2016}
}

References

  1. S. Johnson and M. Everingham. Clustered pose and nonlinear appearance models for human pose estimation. In BMVC, 2010.
  2. B. Sapp and B. Taskar. Modec: Multimodal decomposable models for human pose estimation. In CVPR, 2013.
  3. D. Ramanan. Learning to parse images of articulated objects. In NIPS, 2006.

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Evaluation code for human pose estimation includes strict PCP, PDJ, and PCK.

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