Bayesian Image Based 3D Pose Estimation

@inproceedings{Sanzari2016BayesianIB,
  title={Bayesian Image Based 3D Pose Estimation},
  author={Marta Sanzari and Valsamis Ntouskos and Fiora Pirri},
  booktitle={ECCV},
  year={2016}
}
We introduce a 3D human pose estimation method from single image, based on a hierarchical Bayesian non-parametric model. The proposed model relies on a representation of the idiosyncratic motion of human body parts, which is captured by a subdivision of the human skeleton joints into groups. A dictionary of motion snapshots for each group is generated. The hierarchy ensures to integrate the visual features within the pose dictionary. Given a query image, the learned dictionary is used to… CONTINUE READING
BETA

Similar Papers

Topics from this paper.

Citations

Publications citing this paper.
SHOWING 1-10 OF 36 CITATIONS

Discovery and recognition of motion primitives in human activities

VIEW 8 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

A Study on the Learning Based Human Pose Recognition

  • 2017 9th IEEE-GCC Conference and Exhibition (GCCCE)
  • 2017
VIEW 5 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation

  • 2017 IEEE International Conference on Computer Vision (ICCV)
  • 2017
VIEW 5 EXCERPTS
CITES BACKGROUND, METHODS & RESULTS
HIGHLY INFLUENCED

3D Human Pose Machines with Self-supervised Learning

  • IEEE transactions on pattern analysis and machine intelligence
  • 2019
VIEW 2 EXCERPTS
CITES BACKGROUND