In our recent publication we presented HanCo a dataset that allowed us to surpass ImageNet based pretraining for the task of Hand Shape Estimation from a single RGB image. HanCo is a structured collection of hand images which makes it an ideal testbench for unsupervised representation learning. The collection was aquired by recording short video clips with a calibrated and time sychronized multi-view camera capture setup with a frame rate of 5Hz. This dataset allows to develop unsupervised learning approaches that exploit time and view-based consistency contraints as well as leveraging exact multi-view geometry. Our dataset provides:

In total there are 107,538 time steps available that were recorded by a multi-camera rig with 8 cameras, which results in 860,304 RGB images. Please note, RGB image and camera calibration is the only information that is always available.
For 102,480 time steps a predicted MANO fit is available, from which 9,055 were validated through manual inspection. Hand shape predictions were obtained using the FreiHAND method presented by Zimmermann et al. ICCV 2019.
Foreground segmentation was obtained exploiting the green screen background, which is available for 763,976 images (88.8%).
Hand Segmentation is derived from the Hand Shape annotation and consequently is available on the same frames (102,480*8 = 819,840 frames).
Similarly, 3D hand pose is derived from the hand shape annotation.
Be careful, when using non validated modalities! Their quality does differ substantially.

This dataset is an extended version of the FreiHand dataset.

@InProceedings{Freihand2019,
  author    = {Christian Zimmermann, Duygu Ceylan, Jimei Yang, Bryan Russell, Max Argus and Thomas Brox},
  title     = {FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape from Single RGB Images},
  booktitle = {IEEE International Conference on Computer Vision (ICCV)},
  year = {2019},
  url          = "https://lmb.informatik.uni-freiburg.de/projects/freihand/"
}

@InProceedings{ZimmermannAB21,
  author    = {Christian Zimmermann, Max Argus, and Thomas Brox},
  title     = {Contrastive Representation Learning for Hand Shape Estimation}
  booktitle = {arxive},
  year = {2021},
  url          = "https://lmb.informatik.uni-freiburg.de/projects/hanco/"
}