• Overview
  • Video Presentation
  • Subjects and Activities
  • Data
  • Mixed Reality
  • Code and Features
  • Acknowledgements

Diversity and Size

  • • 3.6 million 3D human poses and corresponding images

  • • 11 professional actors (6 male, 5 female)

  • • 17 scenarios (discussion, smoking, taking photo, talking on the phone...)

Accurate Capture and Synchronization

  • • High-resolution 50Hz video from 4 calibrated cameras

  • • Accurate 3D joint positions and joint angles from high-speed motion capture system

  • • Time-of-flight range data

  • • 3D laser scans of the actors

  • • Accurate background subtraction, person bounding boxes

Support for Development

  • • Precomputed image descriptors

  • • Software for visualization and discriminative human pose prediction

  • • Performance evaluation on withheld test set


References

The datasets, large-scale learning techniques, and related experiments are described in:

  • Catalin Ionescu, Dragos Papava, Vlad Olaru and Cristian Sminchisescu, Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014 [pdf][bibtex]

  • Catalin Ionescu, Fuxin Li and Cristian Sminchisescu, Latent Structured Models for Human Pose Estimation, International Conference on Computer Vision, 2011 [pdf][bibtex]

The license agreement for data usage implies the citation of the two papers above. Please notice that citing the dataset URL instead of the publications would not be compliant with this license agreement.