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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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 42; H. 10; S. 2540 - 2551
Hauptverfasser: Hesse, Nikolas, Pujades, Sergi, Black, Michael J., Arens, Michael, Hofmann, Ulrich G., Schroeder, A. Sebastian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Schlagworte:
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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Zusammenfassung: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.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2019.2917908