  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. Our motivation is the 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 optimize 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 for both the hand and object. This dataset is currently made of
77,558 frames, 68 sequences, 10 persons, and 10 objects. Using our dataset, we
develop a single RGB image-based method to predict the hand pose when
interacting with objects under severe occlusions and show it generalizes to
objects not seen in the dataset.
