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 cooperativ...

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Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 42; číslo 10; s. 2540 - 2551
Hlavní autoři:	Hesse, Nikolas, Pujades, Sergi, Black, Michael J., Arens, Michael, Hofmann, Ulrich G., Schroeder, A. Sebastian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.10.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Témata:	Animals Avatars Biological system modeling Body models Computer Science Computer Vision and Pattern Recognition Data models data-driven Face Human body Infants Learning motion analysis RGB-D Shape Statistical models Three dimensional bodies Three dimensional models Three-dimensional displays RGB-D Body models infants data-driven motion analysis
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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Abstract	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.
AbstractList	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. 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.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.
Author	Arens, Michael Schroeder, A. Sebastian Hofmann, Ulrich G. Hesse, Nikolas Black, Michael J. Pujades, Sergi
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Cites_doi	10.1145/882262.882311 10.1007/978-3-642-33709-3_59 10.1016/j.patcog.2017.02.018 10.1109/ICCV.2015.265 10.1109/CVPR.2017.494 10.1145/3072959.3073685 10.1109/CVPR.2013.21 10.1145/280814.280823 10.1109/CVPR.2017.586 10.1109/TPAMI.2012.68 10.1109/CVPR.2017.143 10.1145/1073204.1073207 10.1007/978-3-030-00928-1_89 10.1109/CVPR.2017.582 10.1109/CVPR.2018.00761 10.1145/311535.311556 10.1007/978-3-319-10470-6_54 10.1145/2766993 10.1111/cgf.12838 10.1007/978-3-642-33783-3_18 10.1109/TIFS.2013.2291969 10.1145/3130800.3130883 10.1109/3DV.2018.00062 10.1145/2816795.2818013 10.1109/CVPR.2018.00055 10.1145/3072959.3073711 10.1109/CVPR.2014.301 10.3389/fneur.2014.00284 10.1007/978-3-030-11024-6_3 10.1007/978-3-642-33783-3_53 10.1109/CVPR.2016.511 10.1109/3DV.2013.44 10.1007/978-3-319-10584-0_11 10.1007/978-3-319-46720-7_57 10.1007/978-3-319-46454-1_34 10.1109/3DV.2018.00022 10.1109/CVPR.2014.92 10.1109/CVPR.2017.492 10.1145/358669.358692 10.1111/j.1467-8659.2009.01373.x 10.1016/j.gaitpost.2012.03.033 10.1016/0378-3782(90)90011-7 10.1109/ICCV.2011.6126465 10.1109/TVCG.2015.2478779 10.1145/641485.641487 10.1016/j.jpeds.2004.05.017 10.1109/CVPR.2017.591
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References	ref13 ref12 ref15 ref14 ref11 ref10 geman (ref44) 1987; 52 loper (ref50) 0 ref17 ref16 ref19 ref18 ref51 ref46 ref45 ref48 ref47 ref42 ref41 ref43 ref8 ref7 ref9 ref4 ref3 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 nocedal (ref49) 2006 ref24 ref23 ref26 tung (ref6) 2017 ref25 ref20 ref22 ref21 ref28 ref27 ref29
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SubjectTerms	Animals Avatars Biological system modeling Body models Computer Science Computer Vision and Pattern Recognition Data models data-driven Face Human body Infants Learning motion analysis RGB-D Shape Statistical models Three dimensional bodies Three dimensional models Three-dimensional displays
Title	Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences
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