Posebits for Monocular Human Pose Estimation

@inproceedings{PonsMoll_CVPR2014,
  title = {Posebits for Monocular Human Pose Estimation},
  booktitle = { Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
  abstract = {We advocate the inference of qualitative information about 3D human 
  pose, called posebits, from images.  Posebits represent boolean 
  geometric relationships between body parts 
  (e.g., left-leg in front of right-leg or hands close to each other).
  The advantages of posebits as a mid-level representation are 1) 
  for many tasks of interest, such qualitative
  pose information may be sufficient (e.g. , semantic image retrieval), 
  2) it is relatively easy to annotate large image corpora with posebits, as 
  it simply requires answers to yes/no questions; and 3) they help 
  resolve challenging pose ambiguities and therefore
  facilitate the difficult talk of image-based 3D pose estimation.
  We introduce posebits, a posebit database, a method for selecting useful 
  posebits for pose estimation and a structural SVM model for posebit inference.  
  Experiments show the use of posebits for semantic image 
  retrieval and for improving 3D pose estimation.},
  pages = {2345--2352},
  address = {Columbus, Ohio, USA},
  month = jun,
  year = {2014},
  author = {Pons-Moll, Gerard and Fleet, David J. and Rosenhahn, Bodo},
  month_numeric = {6}
}