3D Poses in the Wild Dataset

Recovering Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera

Timo von Marcard1, Roberto Henschel1, Michael Black2, Bodo Rosenhahn2 and Gerard Pons-Moll3

1TNT, Leibniz University of Hannover 
2Max Planck Institute for Intelligent Systems
3Max Planck Institute for Informatics, Saarland Informatics Campus

ECCV 2018 Munich, Germany


This dataset may be used for different tasks. If you use the dataset to evaluate human pose and shape estimation, please look at the protocols and metrics below.


The data in sequenceFiles.zip contains the sequences separated in three folders: train/, validation/, test/. In order to be able to compare different methods, we define the following evaluation protocols. Please, when you report results, indicate which of the above protocols you use.


We strongly encourage you to report some or all of the following metrics in your report: PROCRUSTES: Many methods do procrustes alignment before computing the error. We recommend reporting the result using Procrustes alignment (root orientatio, translation and scale) and without.


Note: we use the term "ground truth" to refer to our reference poses whose accuracy has been validated in the paper.


To download, you have to first read and agree the license terms:

  • License

  • Requirements

    To run the example scripts, you will need the following:
  • Numpy & Scipy
  • Chumpy
  • OpenCV (2)
  • OpenDR
  • SMPL

  • Citation

    If you use this dataset, you agree to cite the corresponding ECCV'18 paper:
        title = {Recovering Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera},
        author = {von Marcard, Timo and Henschel, Roberto and Black, Michael and Rosenhahn, Bodo and Pons-Moll, Gerard},
        booktitle = {European Conference on Computer Vision (ECCV)},
        year = {2018},
        month = {sep}