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Exploring the space of human body shapes: data-driven synthesis under anthropometric control . Brett Allen Brian Curless Zoran Popović University of Washington. 2004-01-2188. Motivation. Traditional anthropometry has focused on sets of one-dimensional measurements. ?. Motivation.

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exploring the space of human body shapes data driven synthesis under anthropometric control

Exploring the space of human body shapes:data-driven synthesisunder anthropometric control

Brett Allen

Brian Curless

Zoran Popović

University of Washington

2004-01-2188

motivation
Motivation

Traditional anthropometry has focused on sets of

one-dimensional measurements.

motivation1

?

Motivation
  • Full body shape capture promises to advance the
  • state of the art.
caesar
CAESAR
  • Civilian 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).

scan detail

surface color

74 markers

Scan detail
  • ~250,000 triangles
  • incomplete coverage
overview
Overview
  • 1. Introduction
  • 2. Building a model
  • 3. Synthesis & editing
overview1
Overview
  • 1. Introduction
  • 2. Building a model
  • 3. Synthesis & editing
matching algorithm
Matching algorithm
  • Find the shape that:
  • 1. Matches the template markers to the scanned markers

2. Moves template vertices to scanned surface

3. Minimizes the deformation

template

scan

overview2
Overview
  • 1. Introduction
  • 2. Building a model
  • 3. Synthesis & editing
statistical analysis

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Statistical analysis

mean + PCA

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statistical analysis1

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Statistical analysis

mean + PCA

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statistical analysis2

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Statistical analysis

mean + PCA

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fitting to attributes
Fitting to attributes
  • We can correlate the PCA reconstructions of our scanned people with known attributes:
fitting to points

user constraint

optimized reconstruction

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

summary
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
future work
Future work
  • - analyze shape variation between poses
future work1
Future work
  • - combine with anatomical models and physical simulation

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Aubel 2003

acknowledgments
Acknowledgments
  • - Kathleen Robinette and the CAESAR project
  • - Ethel Evans
  • - Domi Pitturo
  • - Daniel Wood
  • - NSF
  • - NSERC
  • - Microsoft Research, Electronic Arts, Sony
  • - University of Washington Animation Research Labs

2004-01-2188