  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 that suggests candidate 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 method recovers full-body 2D and 3D poses,
hallucinating plausible body parts when the persons are partially occluded or
truncated by the image boundary. 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 and demonstrates satisfying
3D pose results even for multi-person images.
