  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.
