Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles IEEE Transactions on Pattern Analysis and Machine Intelligence Year : 2020

Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences

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.
Fichier principal
Vignette du fichier
smil_tpami_final.pdf (12.46 Mo) Télécharger le fichier
Vignette du fichier
thumb_lg_HessePAMI.png (74.76 Ko) Télécharger le fichier
thumb_xl_HessePAMI.png (170.37 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Format : Figure, Image
Loading...

Dates and versions

hal-02162171 , version 1 (21-06-2019)

Identifiers

Cite

Nikolas Hesse, Sergi Pujades, Michael J Black, Michael Arens, Ulrich Hofmann, et al.. Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42 (10), pp.2540-2551. ⟨10.1109/TPAMI.2019.2917908⟩. ⟨hal-02162171⟩
220 View
234 Download

Altmetric

Share

Gmail Facebook X LinkedIn More