Learning a correlated model of identity and pose-dependent body shape variation for real-time...

Learning a correlated model of identity and pose-dependent body shape variation

for real-time synthesis

Brett Allen1,2, Brian Curless1, Zoran Popović1, and Aaron Hertzmann3

1 University of Washington2 Industrial Light & Magic

3 University of Toronto

Motivation

movies games

telepresence design

Goal

• We would like to be able to generate body models of any individual in any pose.

Identity +

pose shape

- want to synthesize models in real-time

- model should be learnable from real data

Data

• CAESAR data set: 44 subjects in 2 poses• Multi-pose data set: 5 subjects in 16 poses• Dense-pose data set: 1 subject in 69 poses

[Anguelov et al. 2005]

Related work

Anatomical methods

Aubel 2002

Chadwick et al. 1989Turner and Thalmann 1993Scheepers et al. 1997Wilhelms and Van Gelder 1997…

Example-based methods

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v = shape vectorp = example parameters = function parameters

Given: n examples v0…vn and n sets of parameters p0…pn (optional)find .

Scattered data interpolation

Allen et al. 2002

Lewis et al. 2000Sloan et al. 2001Kry et al. 2002

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iif φΦp ),(columns of = key shapesi = reconstruction weights from applying k-nearest neighbors or RBFs on p

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An aside on enveloping

Enveloping + scattered data interpolation= “corrective enveloping”

Latent variable modelsvWxvWx }),{,(f

x = latent variable (component weights)W = components in columnsv = average shape

Blanz & Vetter 1999

Allen et al 2003Seo et al 2003Anguelov et al 2005…

Pose variation vs body variation

Sloan et al. 2001

Pose variation vs body variation

Anguelov et al. 2005

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Our approach

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c = “character vector”: all information needed to put a character in any posev = shape in a particular pose

Intrinsic skeletonparameters: bonelengths and carrying angles

Two Problems

1. Scans might not be at “key” poses

2. Scans are not complete

Maximize: p(c | {e()})

…actually, we don’t know the pose or skinning weights either:

Maximize: p(c, s, q{} | {e()})

Maximum a posteriori estimation

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Results

Going to multiple characters

• One possibility: Learn several character vectors separately, then run PCA.

• Two problems:– the character vector contains values that have

different scales (rest positions, offsets, bone lengths)

– we don’t have enough data!

Identity variation

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NNg ii I0xqsce

cWxc

~ ~

c is the character vector of the th example persong(c,s,q) applies skinning and pose space deformations

Alternating optimization

• We initialize the {x} with the weights from running PCA on estimated skeleton parameters.

• We then optimize W, c, s, q.

• Then we optimize for {x}.

• Repeat…

Results

Results

(video)

Conclusions

We present a flexible approach for learning body shape variation between individuals and between poses, including the interrelationship between the two.+ very general: can handle irregular and incomplete sampling in regard to both the poses/identities scanned, and in the surfaces themselves+ the learned model can generate body shapes very quickly (over 75 fps)

Limitations

• You need a lot of data! Our data set was too sparse in some areas.

• Some poses are hard to capture.• It’s very hard to compensate for the

skinning artifacts.• The shape matching could be improved

(high-frequency details are lost if the matching is poor).

Acknowledgements

• UW Animation Research Labs• Washington Research Foundation• National Science Foundation, NSERC, CFI• Microsoft Research, Electronic Arts, Sony, Pixar• Kathleen Robinette and the AFRL lab• Dragomir Anguelov• Domi Pitturo