Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D Joint Detections

@article{Jahangiri2017GeneratingMD,
  title={Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D Joint Detections},
  author={Ehsan Jahangiri and Alan L. Yuille},
  journal={2017 IEEE International Conference on Computer Vision Workshops (ICCVW)},
  year={2017},
  pages={805-814}
}
We propose a method to generate multiple diverse and valid human pose hypotheses in 3D all consistent with the 2D detection of joints in a monocular RGB image. We use a novel generative model uniform (unbiased) in the space of anatomically plausible 3D poses. Our model is compositional (produces a pose by combining parts) and since it is restricted only by anatomical constraints it can generalize to every plausible human 3D pose. Removing the model bias intrinsically helps to generate more… CONTINUE READING