Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Estimate Pose and Shape of Hand-Held Objects from RGB Images   We develop a system for modeling hand-object interactions in 3D from RGB
images that show a hand which is holding a novel object from a known category.
We design a Convolutional Neural Network (CNN) for Hand-held Object Pose and
Shape estimation called HOPS-Net and utilize prior work to estimate the hand
pose and configuration. We leverage the insight that information about the hand
facilitates object pose and shape estimation by incorporating the hand into
both training and inference of the object pose and shape as well as the
refinement of the estimated pose. The network is trained on a large synthetic
dataset of objects in interaction with a human hand. To bridge the gap between
real and synthetic images, we employ an image-to-image translation model
(Augmented CycleGAN) that generates realistically textured objects given a
synthetic rendering. This provides a scalable way of generating annotated data
for training HOPS-Net. Our quantitative experiments show that even noisy hand
parameters significantly help object pose and shape estimation. The qualitative
experiments show results of pose and shape estimation of objects held by a hand
"in the wild".

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The categories of this paper are: acc-phys
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The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
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The categories of this paper are: astro-ph
18
The categories of this paper are: econ
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The categories of this paper are: math-ph
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The categories of this paper are: eess
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn
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The categories of this paper are: cond-mat
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The categories of this paper are: math
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The categories of this paper are: supr-con
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The categories of this paper are: plasm-ph
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The categories of this paper are: patt-sol