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FreiHAND Dataset

Dataset for Markerless Capture of Hand Pose and Shape from Single RGB Images

FreiHAND Dataset

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 annotationThe 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.

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University of Freiburg, Germany
https://uni-freiburg.de/en/
Task
3D Reconstruction / Photogrammetry
Annotation Types
Keypoint
Items
Classes
Labels
Models using this dataset
Last updated on 
January 20, 2022
Licensed under 
Research Only
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