Datasets

Freiburg-Berkeley Motion Segmentation Dataset

Video Segmentation Benchmark

Image Sequences

TEM Dataset

TILDA Textile Texture Database

Training data for Exemplar CNN

Generated Matching Dataset

Training data for chair generation

Stereo Ego-motion Dataset

Optical Flow Datasets: "Flying Chairs", "ChairsSDHom"

Scene Flow Datasets

Human Part Segmentation Datasets

Rendered Handpose Dataset

Pedestrian Zone Scene

FreiHAND Dataset

HanCo Dataset

Human Pose RGBD Datasets

OVAD: Open-Vocabulary Attribute Detection Dataset

ADE-OoD: a benchmark for dense Out-of-Distribution detection on natural images.

FreiHAND Dataset

News: An extended version of this dataset with calibration and multiple-views is released in HanCo.
News: Due to ungoing problems with the Codalab evaluation server we have decided to release the evaluation split annotations publicly on this page.

In our recent publication we presented the challenging FreiHAND dataset, a dataset for hand pose and shape estimation from single color image, which can serve both as training and benchmarking dataset for deep learning algorithms. It contains 4*32560 = 130240 training and 3960 evaluation samples. Each training sample provides:

	-	RGB image (224x224 pixels)
	-	Hand segmentation mask (224x224 pixels)
	-	Intrinsic camera matrix K
	-	Hand scale (metric length of a reference bone)
	-	3D keypoint annotation for 21 Hand Keypoints
	-	3D shape annotation

The training set contains 32560 unique samples post processed in 4 different ways to remove the green screen background. Each evaluation sample provides an RGB image, Hand scale and intrinsic camera matrix. The keypoint and shape annotation is withhold and scoring of algorithms is handled through our Codalab evaluation server. For additional information please visit our project page.

Examples

RGB	RGB + Keypoints	RGB + Shape		RGB	RGB + Keypoints	RGB + Shape

Terms of use

This dataset is provided for research purposes only and without any warranty. Any commercial use is prohibited. If you use the dataset or parts of it in your research, you must cite the respective paper.

@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/"
}

Dataset

For examples how to work with the dataset visit the accompanying Github repository.

Download FreiHAND Dataset v2 (3.7GB)
Download FreiHAND Dataset v2 - Evaluation set with annotations (724MB)

Contact

For questions about the dataset please contact Christian Zimmermann.