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Exploring the space of human body shapes:
data-driven synthesisunder anthropometric control
Brett AllenBrian CurlessZoran Popović
University of Washington
2004-01-2188
CAESARCivilian American & European Surface Anthropometry Resource
• thousands of subjects in the U.S. and Europe• traditional anthropometry• demographic survey• laser range scans
We’ll use 250 of these scans (125 male, 125 female).
Matching algorithm
scantemplate
Find the shape that:
1. Matches the template markers to the scanned markers
2. Moves template vertices to scanned surface
3. Minimizes the deformation
Statistical analysis
x0y0z0x1y1z1x2
x0y0z0x1y1z1x2
x0y0z0x1y1z1x2
average male mean + PCA component #1
mean + PCA component #2
Statistical analysis
x0y0z0x1y1z1x2
x0y0z0x1y1z1x2
x0y0z0x1y1z1x2
average male
Statistical analysis
x0y0z0x1y1z1x2
x0y0z0x1y1z1x2
x0y0z0x1y1z1x2
average male mean + PCA component #3
Fitting to attributes
We can correlate the PCA reconstructions of our scanned people with known attributes:
-40
-20
0
20
40
60
1.5 1.7 1.9 2.1
Height (m)
Pri
nci
pal
co
mp
on
ent
#1
Fitting to points Using the distribution of the PCA weights as a prior, we
can find the most likely person that fits a set of point constraints.
PCA variance
user constraint
optimized reconstruction
Summary
Contributions: - an algorithm for creating a consistent
mesh representation from range scan data.
- several ways to explore the variation in human body shape, and to synthesize and edit body models