Learning Human Pose and Motion Models for Animation

Learning Human Pose and Motion Models for Animation

Aaron HertzmannUniversity of Toronto

Animation is maturing …

… but it’s still hard to create

Keyframe animation

Keyframe animation

http://www.cadtutor.net/dd/bryce/anim/anim.html

q1 q2 q3

q(t) q(t)

Characters are very complex

Woody:- 200 facial controls- 700 controls in his body

http://www.pbs.org/wgbh/nova/specialfx2/mcqueen.html

Motion capture

[Images from NYU and UW]

Motion capture

Mocap is not a panacea

Goal: model human motion

What motions are likely?

Applications:• Computer animation• Computer vision

Related work: physical models

•Accurate, in principle•Too complex to work with

(but see [Liu, Hertzmann, Popović 2005])

•Computationally expensive

Related work: motion graphs

Input: raw motion capture

“Motion graph”(slide from J. Lee)

Approach: statistical models of motions

Learn a PDF over motions, and synthesize from this PDF [Brand and Hertzmann 1999]

What PDF do we use?

Style-Based Inverse Kinematics

with: Keith Grochow, Steve Martin, Zoran Popović

Motivation

Body parameterization

Pose at time t: qt

Root pos./orientation (6 DOFs)

Joint angles (29 DOFs)

MotionX = [q1, …, qT]

Forward kinematics

Pose to 3D positions:

qt

[xi,yi,zi]t

FK

Problem Statement

Generate a character pose based on a chosen style subject to constraints

Constraints

Degrees of freedom (DOFs) q

Real-time Pose Synthesis

Off-Line Learning

Approach

Motion Learning Style

Synthesis

Pose

Constraints

y(q) = q orientation(q) velocity(q) [ q0 q1 q2 …… r0 r1 r2 v0 v1 v2 … ]

Features

Goals for the PDF

• Learn PDF from any data

• Smooth and descriptive

• Minimal parameter tuning

• Real-time synthesis

Mixtures-of-Gaussians

GPLVM

y1

y2

y3

x1

x2

Latent Space Feature Space

Gaussian Process Latent Variable Model [Lawrence 2004]

GP

-1

x ~ N(0,I)y ~ GP(x; )

Learning: arg max p(X, | Y) = arg max p(Y | X, ) p(X)

Scaled Outputs

Different DOFs have different “importances”

Solution:RBF kernel function k(x,x’)ki(x,x’) = k(x,x’)/wi

2

Equivalently: learn x Wywhere W = diag(w1, w2, … wD)

Precision in Latent Space

2(x)

);(ln2);(2

);();(L 2

2

2

IK θxθx

θ)f(xyWθyx, θ

D

SGPLVM Objective Function

y1

y2

y3

x1

x2 θ)f(x;y

xx

C

Baseball Pitch

Track Start

Jump Shot

Style interpolation

Given two styles 1 and 2, can we “interpolate” them?

));(exp()(1 1θyy IKLp

Approach: interpolate in log-domain

));(exp()(2 2θyy IKLp

Style interpolation

));(exp()( 22 θyy IKLp ));(exp()(1 1θyy IKLp

(1-s) s

)(s)()s1( 21 ypyp

Style interpolation in log space

));(exp( 1θyIKL ));(exp( 1θyIKL

(1-s)s

));(s);()s1((exp( 21 θyθy LL

Interactive Posing

Interactive Posing

Interactive Posing

Multiple motion style

Realtime Motion Capture

Style Interpolation

Trajectory Keyframing

Posing from an Image

Modeling motion

GPLVM doesn’t model motions• Velocity features are a hack How do we model and learn dynamics?

Gaussian Process Dynamical Models

with: David Fleet, Jack Wang

Dynamical models

xt+1xt

Hidden Markov Model (HMM)Linear Dynamical Systems (LDS)

[van Overschee et al ‘94; Doretto et al ‘01]

Switching LDS[Ghahramani and Hinton ’98; Pavlovic et al ‘00; Li et al ‘02]

Nonlinear Dynamical Systems[e.g., Ghahramani and Roweis ‘00]

Dynamical models

Gaussian Process Dynamical Model (GPDM)

Marginalize out , and then optimize the latent positions to simultaneously minimize pose reconstruction error and (dynamic) prediction error on training data .

pose reconstruction

latent dynamics

Latent dynamical model:

Assume IID Gaussian noise, and

with Gaussian priors on and

DynamicsThe latent dynamic process on

has a similar form:

where

is a kernel matrix defined by kernel function

with hyperparameters

Subspace dynamical model:

Markov Property

Remark: Conditioned on , the dynamical model is 1st-order Markov, but the marginalization introduces longer temporal dependence.

Learning

To estimate the latent coordinates & kernel parameters we minimize

with respect to and .

GPDM posterior:

reconstruction likelihood

priorsdynamics likelihood

training motions

hyperparameterslatent trajectories

Motion Capture Data

~2.5 gait cycles (157 frames) Learned latent coordinates (1st-order prediction, RBF kernel)

56 joint angles + 3 global translational velocity + 3 global orientation from CMU motion capture database

3D GPLVM Latent Coordinates

large “jumps’ in latent space

Reconstruction Variance

Volume visualization of .

(1st-order prediction, RBF kernel)

Motion Simulation

Animation of mean motion (200 step sequence)

initial state

Random trajectories from MCMC (~1 gait cycle, 60 steps)

Simulation: 1st-Order Mean Prediction

Red: 200 steps of mean prediction

Green: 60-step MCMC mean

Animation

Missing Data

50 of 147 frames dropped (almost a full gait cycle)

spline interpolation

Missing Data: RBF Dynamics

Determining hyperparameters

GPDM Neil’s parameters MCEM

Data: six distinct walkers

Where do we go from here?

Let’s look at some limitations of the model

60 Hz 120 Hz

What do we want?

Phase

Variation

x1

x2

A walk cycle

Branching motions

Walk Run

Stylistic variation

Current work: manifold GPs

Latent space (x) Data space (y)

Summary

GPLVM and GPDM provide priors from small data sets

Dependence on initialization, hyperpriors, latent dimensionality

Open problems modeling data topology and stylistic variation