Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning and Tracking the 3D Body Shape of Freely Moving Infants from
  RGB-D sequences   Statistical models of the human body surface are generally learned from
thousands of high-quality 3D scans in predefined poses to cover the wide
variety of human body shapes and articulations. Acquisition of such data
requires expensive equipment, calibration procedures, and is limited to
cooperative subjects who can understand and follow instructions, such as
adults. We present a method for learning a statistical 3D Skinned Multi-Infant
Linear body model (SMIL) from incomplete, low-quality RGB-D sequences of freely
moving infants. Quantitative experiments show that SMIL faithfully represents
the RGB-D data and properly factorizes the shape and pose of the infants. To
demonstrate the applicability of SMIL, we fit the model to RGB-D sequences of
freely moving infants and show, with a case study, that our method captures
enough motion detail for General Movements Assessment (GMA), a method used in
clinical practice for early detection of neurodevelopmental disorders in
infants. SMIL provides a new tool for analyzing infant shape and movement and
is a step towards an automated system for GMA.

27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph