Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences Article Swipe
Nikolas Hesse
,
Sergi Pujades
,
Michael J. Black
,
Michael Arens
,
Ulrich Hofmann
,
A. Sebastian Schroeder
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/tpami.2019.2917908
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/tpami.2019.2917908
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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/tpami.2019.2917908
- https://ieeexplore.ieee.org/ielx7/34/9185119/08732396.pdf
- OA Status
- bronze
- Cited By
- 13
- References
- 64
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2896742661
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2896742661Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tpami.2019.2917908Digital Object Identifier
- Title
-
Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequencesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-06-06Full publication date if available
- Authors
-
Nikolas Hesse, Sergi Pujades, Michael J. Black, Michael Arens, Ulrich Hofmann, A. Sebastian SchroederList of authors in order
- Landing page
-
https://doi.org/10.1109/tpami.2019.2917908Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/34/9185119/08732396.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/34/9185119/08732396.pdfDirect OA link when available
- Concepts
-
RGB color model, Computer science, Artificial intelligence, Tracking (education), Movement (music), Computer vision, Motion (physics), Calibration, 3d printed, Motion capture, Mathematics, Statistics, Psychology, Aesthetics, Biomedical engineering, Pedagogy, Philosophy, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 2, 2022: 2, 2021: 5, 2020: 3Per-year citation counts (last 5 years)
- References (count)
-
64Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.General | 129 |
| abstract_inverted_index.Skinned | 63 |
| abstract_inverted_index.adults. | 53 |
| abstract_inverted_index.infants | 114 |
| abstract_inverted_index.learned | 9 |
| abstract_inverted_index.limited | 41 |
| abstract_inverted_index.present | 55 |
| abstract_inverted_index.surface | 6 |
| abstract_inverted_index.towards | 162 |
| abstract_inverted_index.variety | 23 |
| abstract_inverted_index.captures | 124 |
| abstract_inverted_index.clinical | 137 |
| abstract_inverted_index.infants. | 77, 97, 146 |
| abstract_inverted_index.learning | 59 |
| abstract_inverted_index.movement | 157 |
| abstract_inverted_index.practice | 138 |
| abstract_inverted_index.properly | 89 |
| abstract_inverted_index.provides | 148 |
| abstract_inverted_index.requires | 34 |
| abstract_inverted_index.subjects | 44 |
| abstract_inverted_index.Movements | 130 |
| abstract_inverted_index.analyzing | 153 |
| abstract_inverted_index.automated | 164 |
| abstract_inverted_index.detection | 141 |
| abstract_inverted_index.disorders | 144 |
| abstract_inverted_index.expensive | 35 |
| abstract_inverted_index.generally | 8 |
| abstract_inverted_index.sequences | 73, 110 |
| abstract_inverted_index.thousands | 11 |
| abstract_inverted_index.Assessment | 131 |
| abstract_inverted_index.equipment, | 36 |
| abstract_inverted_index.factorizes | 90 |
| abstract_inverted_index.faithfully | 83 |
| abstract_inverted_index.predefined | 17 |
| abstract_inverted_index.represents | 84 |
| abstract_inverted_index.understand | 47 |
| abstract_inverted_index.Acquisition | 30 |
| abstract_inverted_index.Statistical | 0 |
| abstract_inverted_index.calibration | 37 |
| abstract_inverted_index.cooperative | 43 |
| abstract_inverted_index.demonstrate | 99 |
| abstract_inverted_index.experiments | 79 |
| abstract_inverted_index.incomplete, | 70 |
| abstract_inverted_index.low-quality | 71 |
| abstract_inverted_index.procedures, | 38 |
| abstract_inverted_index.statistical | 61 |
| abstract_inverted_index.Multi-Infant | 64 |
| abstract_inverted_index.Quantitative | 78 |
| abstract_inverted_index.high-quality | 13 |
| abstract_inverted_index.applicability | 101 |
| abstract_inverted_index.instructions, | 50 |
| abstract_inverted_index.articulations. | 29 |
| abstract_inverted_index.neurodevelopmental | 143 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.80489044 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |