Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: NeuralAnnot: Neural Annotator for 3D Human Mesh Training Sets   Most 3D human mesh regressors are fully supervised with 3D pseudo-GT human
model parameters and weakly supervised with GT 2D/3D joint coordinates as the
3D pseudo-GTs bring great performance gain. The 3D pseudo-GTs are obtained by
annotators, systems that iteratively fit 3D human model parameters to GT 2D/3D
joint coordinates of training sets in the pre-processing stage of the
regressors. The fitted 3D parameters at the last fitting iteration become the
3D pseudo-GTs, used to fully supervise the regressors. Optimization-based
annotators, such as SMPLify-X, have been widely used to obtain the 3D
pseudo-GTs. However, they often produce wrong 3D pseudo-GTs as they fit the 3D
parameters to GT of each sample independently. To overcome the limitation, we
present NeuralAnnot, a neural network-based annotator. The main idea of
NeuralAnnot is to employ a neural network-based regressor and dedicate it for
the annotation. Assuming no 3D pseudo-GTs are available, NeuralAnnot is weakly
supervised with GT 2D/3D joint coordinates of training sets. The testing
results on the same training sets become 3D pseudo-GTs, used to fully supervise
the regressors. We show that 3D pseudo-GTs of NeuralAnnot are highly beneficial
to train the regressors. We made our 3D pseudo-GTs publicly available.

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The categories of this paper are: astro-ph
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The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: supr-con
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The categories of this paper are: q-fin
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ph
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The categories of this paper are: nlin
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The categories of this paper are: mtrl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: gr-qc
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The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: q-alg