Presentation made @3DBODY.TECH Lugano, 17th October 2018.
This paper presents partial results of a larger validation study of different Data-driven 3D Reconstruction (D3DR) technologies developed by IBV to create watertight 3D human models from measurements (1D3D), 2D images (2D3D) or raw scans (3D3D). This study quantifies the reliability (Standard Error of Measurement, SEM; Mean Absolute Deviation, MAD; Intra-class Correlation Coefficient, ICC; and Coefficient of Variation, CV) of body measurements taken on human subjects. Our results are also compared to similar studies found in literature assessing the reliability of digital and traditional anthropometry. Moreover, we assess the compatibility (bias and Mean Absolute Error, MAE) of measurements between D3DR technologies. The results show that 2D3D can provide visually accurate body shapes and, for the measurements assessed, 2D3D is as reliable as high-resolution 3D scanners. It is also more accurate than manual measurements taken by untrained users. Due to accessibility, cost and portability (e.g. 2D3D built in a smartphone app) they could be more suitable than other methods at locations where body scanners are not available such as homes, medical or physical therapy offices, and small retail stores and gyms.
3D Human Models from 1D, 2D & 3D Inputs @3DBODY.TECH 17th Oct 2018
1. 3D human models
from 1D, 2D & 3D inputs
reliability and compatibility
of body measurements
Alfredo Ballester
Anthropometry Research Group of IBV
alfredo.ballester@ibv.org
3. IBV is a private not-for-profit R&D organisation
Consultancy
for manufacturing industries
Research & Development
for technology companies
Apparel Sports Transport
Health
Safety
Leisure
Appliances Elderly
Orthotics
Motion Analysis
Anthropometry
Human Factors
4. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Digital Anthropometry at IBV
2004 Start gathering 3D foot scan data
2007 Start gathering body scan data
2012 Start developing own automatic 3D
processing SW for research
2018 Launch of 3D BODY reconstruction
with smartphone photographs
2015 Launch of 3D FOOT reconstruction
with smartphone photographs
5. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Data-driven
3D Recons-
truction
Data-driven 3D Reconstruction
2D3D
1D3D
3D3D
human shape & pose
data model learnt
from large 3D databases
Virtual Fashion
Virtual
Ergonomics
Measurements Joints3D model
7. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
3D3D – Raw scans to 3D models
Point
Cloud
Incomplete
or noisy
mesh
Artefacted
mesh
Watertight
complete
model
Markerless
(A-Pose)
Robust
Automatic
Fast
Adjustable to
input quality
8. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
3D3D – Raw scans to 3D models
Anatomical surface completion Anatomical correction of artefacts and noise
9. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
3D3D – Raw scans to 3D models
• 3Dfy.me
• 3dMD
• 4Ddynamics
• CyberWare
• Human Solutions
• Fit3D
• H3ALTH TECH.
• Lemotive
• NOMO
• Passen
• Scanologics
• ShapeMe
• Artec
• SizeStream
• SpaceVision
• Telmat
• TC2
• Treedys
• Twinster
• Voxelan
• Youdome
CAESAR Size Korea Sizing Portugal Size UK Spanish Survey HQL Japan Smartfit Belgium
10. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
2D3D – Images to 3D models
2 images, gravity sensor
& camera parameters
Segmentation
& keypoints
(deep learning)
3D Reconstruction
Measuring, rigging, etc.
3D Body Model
Projection
matrix
estimation
Data-Driven
Space of Shapes of
Human Body
Guiding outline
Data-Driven
Space of Body
outlines
Age, weight, height
Ballester et al. 2016 [43]
11. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
2D3D – deep learning improvements
old
method [43]
new
method
Poor guide
outline fit
2 images, gravity sensor
& camera parameters
Segmentation
& keypoints
(deep learning)
3D Reconstruction
Measuring, rigging, etc.
3D Body Model
Projection
matrix
estimation
Data-Driven
Space of Shapes of
Human Body
Guiding outline
Data-Driven
Space of Body
outlines
Age, weight, height
12. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
2D3D – deep learning improvements
2 images, gravity sensor
& camera parameters
Segmentation
& keypoints
(deep learning)
3D Reconstruction
Measuring, rigging, etc.
3D Body Model
Projection
matrix
estimation
Data-Driven
Space of Shapes of
Human Body
Guiding outline
Data-Driven
Space of Body
outlines
Age, weight, height
13. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
2D3D – deep learning improvements
old method [43]
2 images, gravity sensor
& camera parameters
Segmentation
& keypoints
(deep learning)
3D Reconstruction
Measuring, rigging, etc.
3D Body Model
Projection
matrix
estimation
Data-Driven
Space of Shapes of
Human Body
Guiding outline
Data-Driven
Space of Body
outlines
Age, weight, height
new method
14. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
2D3D – deep learning improvements
old method new method
2 images, gravity sensor
& camera parameters
Segmentation
& keypoints
(deep learning)
3D Reconstruction
Measuring, rigging, etc.
3D Body Model
Projection
matrix
estimation
Data-Driven
Space of Shapes of
Human Body
Guiding outline
Data-Driven
Space of Body
outlines
Age, weight, height
15. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
2D3D – deep learning improvements
Back leg
visible
Back leg
visible
Lumbar
occlusion
2 images, gravity sensor
& camera parameters
Segmentation
& keypoints
(deep learning)
3D Reconstruction
Measuring, rigging, etc.
3D Body Model
Projection
matrix
estimation
Data-Driven
Space of Shapes of
Human Body
Guiding outline
Data-Driven
Space of Body
outlines
Age, weight, height
16. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
1D3D – Parameters to 3D models
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⋯ 𝑝𝑝𝑚𝑚
𝑛𝑛
𝑛𝑛,𝑚𝑚
𝒀𝒀 = 𝒀𝒀𝟎𝟎 + �𝑩𝑩𝑷𝑷𝑷𝑷𝑷𝑷 · (𝑿𝑿 − 𝑿𝑿𝟎𝟎) + �𝑭𝑭
𝑌𝑌 =
𝑡𝑡𝑃𝑃𝑃𝑃1
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𝑛𝑛
𝑛𝑛,𝑝𝑝
Input parameters (X) can be
body measurements or other
metrics (e.g. age or weight)
17. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Data-driven 3D Reconstruction
Accuracy of the 3D model
• Age
• Weight
• Height
• Waist
• Hips
• …
1D-3D 2D-3D 3D-3D LoQ 3D-3D HiQ
19. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Body shape variability due to:
Pose, muscle contraction,
respiration, garments, etc…
Objectives of the experiment
#2 Assessment of the REALIBILITY of measurements
from 2D3D and 3D3D
• Quantification of errors: SEM, MAD, ICC, CV
• Comparison with 20 similar studies using 3D body
scanners and Expert manual measurements
#3 Assessment of the COMPATIBILITY of measurements
between 3D3D and the other techs, 2D3D and 1D3D
• Quantification of errors: Bias and MAE
#1 Visual Assessment of body SHAPE ACCURACY of
2D3D and 1D3D wrt 3D3D
20. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Design of the experiment
Method Input data
1D3D(3) Age, Height, Weight
1D3D(6) Age, Height, Weight, Chest girth, Waist girth, Hip girth
1D3D(7) Age, Height, Weight, Chest girth, Waist girth, Hip girth, Crotch height
2D3D Age, Height, Weight, front image, side image
3D3D Raw 3D scan
Participants
• 77 (39♀ 38♂) volunteers
• Variety of body shapes
o Weight 44-136 kg
o Height 149-189 cm
o Age 19-58 y.o.
Equipment
• Vitus XXL (Human Solutions)
• Motorola Nexus 6
• Self-reported measurements
taken at home (37 users)
3D processing
Procedure
• Skin-tight clothing
• A-Pose
• 2 repetitions with repositioning
32. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Conclusions
Method Qualitative Assessment Quantitative Assessment
• Visually perfect results
• Surface-to-scan accuracy adjustable to
accuracy of input
• MAD 0.1-0.5 cm, SEM 0.2-0.8 cm
• ICC > 0.98, CV < 2%
• Realistic and visually
accurate 3D shapes for all
body types
• Accurate and reliable measurements
• MAD 0.2-0.6 cm, SEM 0.3-1 cm
• ICC > 0.93, CV < 2%
• MAE 0.4-2.2 cm
• Indicative body shapes
• Body shapes tend to average
• Measurements tend to average
• Accuracy is highly dependent on user skills
• MAE 0.7-4.6 cm
3D3D
1D3D
2D3D
33. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Ongoing research 3D3D
Objectives: Any pose, clearing scene of objects, noise, floor
Methods: deep learning for automatic landmarking in any
pose and noise filtering
3D3D modelShape+Pose+
+Soft tissue
Shape Shape+Pose
34. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Ongoing research 2D3D
Objectives: less restrictive input such as casual clothing and more relaxed/natural poses
Methods: different alternatives but all making intensive use of deep learning
35. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Presentation References (numbering of the paper)
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36. Thank you!
Sandra Alemany
Ana Piérola
Eduardo Parrilla
Jordi Uriel
Alfredo Remón
Juan A. Solves
Ana V. Ruescas
Julio A. Vivas
Juan V. Durá
Alfredo Ballester
Juan C. González
Beatriz Mañas
Rosa Porcar
https://antropometria.ibv.org/en/
Youtube Channel: https://www.youtube.com/channel/UChFTNRmt3veDBWuVoJsugTg
Full Paper: http://www.3dbody.tech/cap/papers/2018/18132ballester.pdf