HOnnotate: A method for 3D Annotation of Hand and Objects Poses
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by
Shreyas Hampali, Mahdi Rad, Markus Oberweger, Vincent Lepetit
2020
Abstract
We propose a method for annotating images of a hand manipulating an object
with the 3D poses of both the hand and the object, together with a dataset
created using this method. There is a current lack of annotated real images for
this problem, as estimating the 3D poses is challenging, mostly because of the
mutual occlusions between the hand and the object. To tackle this challenge, we
capture sequences with one or several RGB-D cameras, and jointly optimizes the
3D hand and object poses over all the frames simultaneously. This method allows
us to automatically annotate each frame with accurate estimates of the poses,
despite large mutual occlusions. With this method, we created HO-3D, the first
markerless dataset of color images with 3D annotations of both hand and object.
This dataset is currently made of 80,000 frames, 65 sequences, 10 persons, and
10 objects, and growing. We also use it to train a deepnet to perform RGB-based
single frame hand pose estimation and provide a baseline on our dataset.
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