LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images

@article{Rogez2018LCRNetM2,
  title={LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images},
  author={Gr{\'e}gory Rogez and Philippe Weinzaepfel and Cordelia Schmid},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  year={2018}
}
We propose an end-to-end architecture for joint 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 poses of multiple people simultaneously. Hence, our approach does not require an approximate localization of the humans for initialization. Our Localization-Classification-Regression architecture, named LCR-Net, contains 3 main components: 1) the pose proposal generator… CONTINUE READING

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