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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.


Rendered Handpose Dataset

This dataset has been used to train convolutional networks in our paper Learning to Estimate 3D Hand Pose from Single RGB Images.

It contains 41258 training and 2728 testing samples. Each sample provides:
- RGB image (320x320 pixels)
- Depth map (320x320 pixels)
- Segmentation masks (320x320 pixels) for the classes: background, person, three classes for each finger and one for each palm
- 21 Keypoints for each hand with their uv coordinates in the image frame, xyz coordinates in the world frame and a visibility indicator
- Intrinsic Camera Matrix K
It was created with freely available characters from www.mixamo.com and rendered with www.blender.org. For more details on how the dataset was created please see the mentioned paper.

Examples

RGB + Keypoints Depth Segmentation RGB + Keypoints Depth Segmentation
Image RGB w keypoints DepthMap SegmentationMask Image RGB w keypoints DepthMap SegmentationMask
Image RGB w keypoints DepthMap SegmentationMask Image RGB w keypoints DepthMap SegmentationMask
Image RGB w keypoints DepthMap SegmentationMask Image RGB w keypoints DepthMap SegmentationMask



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.

@TechReport{zb2017hand,
  author    = {Christian Zimmermann and Thomas Brox},
  title     = {Learning to Estimate 3D Hand Pose from Single RGB Images},
  institution    = {arXiv:1705.01389},
  year      = {2017},
  note         = "https://arxiv.org/abs/1705.01389",
  url          = "https://lmb.informatik.uni-freiburg.de/projects/hand3d/"
}



Dataset

The dataset ships with minimal examples, that browse the dataset and show samples. There is one example for Python and one for MATLAB users. See the following README for more information.

Download Rendered Handpose Dataset (7.1GB)



Contact

For questions about the dataset please contact Christian Zimmermann.