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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 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

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  1. Exploring the space of human body shapes:data-driven synthesisunder anthropometric control Brett Allen Brian Curless Zoran Popović University of Washington 2004-01-2188

  2. Motivation Traditional anthropometry has focused on sets of one-dimensional measurements.

  3. ? Motivation • Full body shape capture promises to advance the • state of the art.

  4. 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).

  5. surface color 74 markers Scan detail • ~250,000 triangles • incomplete coverage

  6. Overview • 1. Introduction • 2. Building a model • 3. Synthesis & editing

  7. Overview • 1. Introduction • 2. Building a model • 3. Synthesis & editing

  8. The Correspondence Problem

  9. 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

  10. Matching algorithm

  11. Overview • 1. Introduction • 2. Building a model • 3. Synthesis & editing

  12. x0 x0 x0 y0 y0 y0 z0 z0 z0 x1 x1 x1 y1 y1 y1 z1 z1 z1 x2 x2 x2 average male Statistical analysis mean + PCA component #1

  13. x0 x0 x0 y0 y0 y0 z0 z0 z0 x1 x1 x1 y1 y1 y1 z1 z1 z1 x2 x2 x2 average male Statistical analysis mean + PCA component #2

  14. x0 x0 x0 y0 y0 y0 z0 z0 z0 x1 x1 x1 y1 y1 y1 z1 z1 z1 x2 x2 x2 average male Statistical analysis mean + PCA component #3

  15. PCA reconstruction

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

  17. Fitting to attributes

  18. 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

  19. 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

  20. Future work • - analyze shape variation between poses

  21. Future work • - combine with anatomical models and physical simulation + Aubel 2003

  22. 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

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