SMIL: Learning an Infant Body Model from RGB-D Data

20 September 2018

01:36

Infant motion analysis enables early detection of neurodevelopmental disorders like cerebral palsy (CP). Diagnosis, however, is challenging, requiring expert human judgement. An automated solution would be beneficial but requires the accurate capture of 3D full-body movements. To that end, we develop a non-intrusive, low-cost, lightweight acquisition system that captures the shape and motion of infants. Going beyond work on modeling adult body shape, we learn a 3D Skinned Multi-Infant Linear body model (SMIL) from noisy, low-quality, and incomplete RGB-D data. We demonstrate the capture of shape and motion with 37 infants in a clinical environment. Quantitative experiments show that SMIL faithfully represents the data and properly factorizes the shape and pose of the infants. With a case study based on general movement assessment (GMA), we demonstrate that SMIL captures enough information to allow medical assessment. SMIL provides a new tool and a step towards a fully automatic system for GMA. Learning an Infant Body Model from RGB-D Data for Accurate Full Body Motion Analysis Hesse, N., Pujades, S., Romero, J., Black, M. J., Bodensteiner, C., Arens, M., Hofmann, U. G., Tacke, U., Hadders-Algra, M., Weinberger, R., Müller-Felber, W., Schroeder, A. S. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2018 pdf: http://files.is.tue.mpg.de/black/papers/miccai18.pdf model: https://s.fhg.de/smil