Animation With Momentum - PowerPoint Presentation

Slide1

Animation With Momentum

Interactive performances with style and substance.

David Wu

Microsoft Game Studios

GDC 2009Slide2

Takeaway

Survey of recent research in the field of interactive animation from the point of view of a game developer.

Assessments of the effectiveness and overall applicability of proposed techniques

A pragmatic distillation of subsets of ideas from the large, fully designed systems presented.

Emphasis on techniques and insights applicable now.Slide3

Straw Man Motivation:

“Animation in Games is not perfect”Da

Silva et al, Siggraph 08

[Video]

Geometric blending used to adapt existing character animations to novel situations (i.e. walk cycle on steps) does not look physically sound.

Characters do not react to external stimulation that has not been planned for (i.e. throwing boxes at people you are talking to)

They remain kinematic, which – from the point of view of physical interactions – is equivalent to having infinite inertiaSlide4

What does an Interactive Animation System provide?

Characters that preserve the personality and style of their animations, seamlessly interact with a physically simulated world and take orders from the gamepad of abusive players and confused AIs.

Figure 1: Physical feasibility is one of many challenges faced by otherwise content virtual people.Slide5

Will Compelling Characters and Physics make my game Sell?

* figures beyond 2009 are estimatesSlide6

SIMBICON: Simple Biped Locomotion Control

SIGGRAPH ‘07Kang Kang

Yin, Kevin

Loken

,

Michiel

van de Panne

Goal is to create a robust control system for bipeds that can:

Balance

Walk with various gaits

Handle external perturbations such as varying terrain and being pushed

Transition between animations

Approximate animations from motion capture

Last goal seems to be treated as an afterthought

Quality and results are not mentioned

Not quantifiable?

This is a first order concern for games.

Robustness as measured by physical simulation results is treated as the highest priority result.Slide7

Van de Panne’s Animation Pipeline

Each animation is a stored as the gains and parameters of one physical controller.

Various controllers exist, each defined by a finite state machine with various parameters and gains.

Each animation corresponds to one controller that has been trained to emulate the animation using physical actuation.

In the absence of external disturbances, the resulting motion mimics the source animation.

In addition to feeding the Mixer, closed loop feedback is required to determine FSM transitions.

(i.e. Heel Strike)

Mixer linearly blends control parameters rather than poses.

Similar controllers are synchronized such that they are in the same state.

A Balance post process is applied. (see next slide for details)

Integrator receives forces and torques, forward dynamics equations are solved and the world state accordingly

Walk Finite State MachineSlide8

SIMBICON: Simple Biped Locomotion Control

SIGGRAPH ’07 (continued)Kang Kang

Yin, Kevin

Loken

,

Michiel

van de Panne

System is based on PD controllers acting at joints

Each animation has its own individual control system that operates the PD gains and targets with values specified by a simple finite state machine.

While this is not treated as a first order concern, the resulting controllers require very little memory.

Compression ratios of 100:1 are typical.Slide9

Balance control system is an adaption of the original

hopper control system developed by Raibert in the ’80s.

Main considerations are foot placement upon landing and inertial forces due to acceleration of legs in flight

SIMBICON: Simple Biped Locomotion Control

SIGGRAPH ’07 (continued)

Kang

Kang

Yin, Kevin

Loken

,

Michiel

van de PanneSlide10

Raiberts

Hopper is Simple and effective. Unfortunately it cannot balance when stationary.Slide11

[Video]

SIMBICON: Simple Biped Locomotion Control

SIGGRAPH ’07 (continued)

Kang

Kang

Yin, Kevin

Loken

,

Michiel

van de PanneSlide12

Continuation Methods forAdapting Simulated Skills

Kangkang

Yin,

Stelian

Coros

Philippe

Beadoin

,

Michiel

van de Panne SIGGRAPH’08

“Simulated characters in simulated worlds require simulated skills.”

An extension of SIMBICON

Machine learning is used to create new animation controllers based on

Existing controllers

New challenges such as variations in terrain, new goals, etc

Learning framework based on numerical optimization employing continuation methods

i.e. Creating an animation controller for pushing a table from a walking motion capture.Slide13

Continuation Methods for

Adapting Simulated Skills

Kangkang

Yin,

Stelian

Coros

Philippe

Beadoin

,

Michiel

van de Panne SIGGRAPH’08

Continuation Methods:Slide14

Continuation Methods forAdapting Simulated Skills

Kangkang

Yin,

Stelian

Coros

Philippe

Beadoin

,

Michiel

van de Panne SIGGRAPH’08

[Video]Slide15

Ad Hoc Meta-analysis of perceived error in physically simulated characters

David Wu, GDC 09

A number of studies have looked at the relative sensitivity of viewers towards various types of error in physical simulation of humans:

Anna

Majkowska

,

Petros

Faloutsos

, Flipping with physics: motion editing for acrobatics,

Eurographics

animation 2007

Yeuhi Abe , C. Karen Liu ,

Zoran

Popovic

, Momentum-based parameterization of dynamic character motion, Graphical Models 2006

Carol O'Sullivan , John

Dingliana , Thanh Giang , Mary K. Kaiser, Evaluating the visual fidelity of physically based animations, SIGGRAPH 2003Paul S. A. Reitsma , Nancy S. Pollard, Perceptual metrics for character animation: sensitivity to errors in ballistic motion, SIGGRAPH 2003Slide16

Ad Hoc Meta-analysis of perceived error in physically simulated characters

Meta findings regarding sensitivity to error:

Linear Momentum: High

Linear Velocity: Moderate

Angular Momentum: Moderate

Angular Velocity: Low

Conservation of Energy: Low

Gravity Magnitude: Inconclusive

Static Balance: High

Dynamic Balance: High

These findings have been cited in various papers, they are considered valid despite the relatively low sample sizes and relatively poor control procedures of the studies.Slide17

Synthesis of Constrained Walking Skills

Stelian Coros

, Philippe

Beaudoin

, Kang

Kang

Yin,

Michiel

van de Panne Siggraph Asia 08

Given a source walk cycle and model, offline optimization is used to generate many single step walk cycle variations

Each variation is generated to with the optimization goal of a random foot placement offset.Slide18

Synthesis of Constrained Walking Skills

Stelian Coros

, Philippe

Beaudoin

, Kang

Kang

Yin,

Michiel

van de Panne Siggraph Asia 08

Variations are assembled to create what the authors call a “

Step-to-Step Dynamics Model”

or an

“SSDM”

.

The step-to-step dynamics model (SSDM). The nonparametric (example-based) model makes predictions using the results of the offline synthesis. The given dimensions for the state and actions spaces are for the 2D bipeds.Slide19

Synthesis of Constrained Walking Skills

Stelian Coros, Philippe

Beaudoin

, Kang

Kang

Yin,

Michiel

van de Panne Siggraph Asia 08

At run time, the controller uses the SSDM with its reference walk cycle to generate a continuous walk cycle that satisfies constraints discovered in real time.

The SSDM itself stores its data as samples compressed via principle component analysis on the differences between learned steps and the reference walk cycle.

Given the strong correlation between variations the authors discover the only 2-4 principle components are required.

Sampling consists of applying an N dimension blend on reference samples and then combining the result with the reference walk cycle.Slide20

Synthesis of Constrained Walking Skills

Stelian Coros, Philippe

Beaudoin

, Kang

Kang

Yin,

Michiel

van de Panne Siggraph Asia 08

Takeaways

The overall framework provides:

A potential alternative to traditional foot placement IK.

Higher quality results

A potential alternative to animator provided walk cycle transitions or variations

Saves animator timeSlide21

Synthesis of Constrained Walking Skills

Stelian Coros, Philippe

Beaudoin

, Kang

Kang

Yin,

Michiel

van de Panne Siggraph Asia 08

Take-Away: PCA Hypothesis

Principle Component Analysis (PCA) is a technique used for the

lossy

compression of vector data sets.

A 3 DOF analogy might be DXT1 compression of a single quad of pixels. In this case only the first Principle component is maintained and this 3D vector is the is the difference between the two end point colors.

The early PCA vectors resulting from all poses of a character are strongly correlated with the signature style and physical characteristics of that character.Slide22

Not Mickey Mouse

Synthesis of Constrained Walking Skills

Stelian

Coros

, Philippe

Beaudoin

, Kang

Kang

Yin,

Michiel

van de Panne Siggraph Asia 08

Take-Away: PCA Hypothesis

When performing inverse kinematics, using PCA vectors as the degrees of freedom and weighting each in a manner inversely proportional to it’s

Eigenvalue

, a least squares IK solution is likely to represent a pose variation that correlates highly with the preferred poses of that character.

Figure 3: 1

st

Principle component vector consists of DOF values that produce a characteristic pose.Slide23

Synthesis of Constrained Walking Skills

Stelian Coros, Philippe

Beaudoin

, Kang

Kang

Yin,

Michiel

van de Panne Siggraph Asia 08

Take-Away: PCA Hypothesis (cont)

There are a few conditions that the dataset used to generate the Principle Component Vectors must meet for this hypothesis to hold:

Should be representative of the entire range of motions preferred poses

Should be representative in a quantitative, statistical sense

Appropriate DOF should form the initial basis

Forces or Poses?

May require doctoring of the input i.e. weighting the std deviation of underrepresented poses.

Figure 4: Animation set has been over-trained. The 1

st

Principle Component vector is not ideal.

Not Mickey MouseSlide24

Flexible Registration of Human Motion Data with Parameterized Motion Models

Yen-Lin Chen, Jian Yuan

Miny

,

Jinxiang

Chaiz

An initial set of animations is collected

Animations are time-warped such that each “cycle” takes 2 seconds

PCA is performed on these animations and the 20 top

Eigenmodes

are maintained

These are run through a “physics filter”, which iteratively modified them until they are physically valid

Figure 9:

First Principle ComponentSlide25

Flexible Registration of Human … (cont)

All animations are projected onto the resulting Principle Components

The authors claim that 20 Eigen modes is enough for mostly lossless representation

New animations are “registered” into the motion database and stored as linear combinations of the components

The authors state the following applications:

Effective motion retargeting

Kinematics, dynamics

and

style

Physics improves believability of motions

Implicitly enforced by the Components

Consistent, realistic motion generation from sparse data.

Registration enforces the constraints implied by the sparse data and fills in the blanks with motion from existing motions.Slide26

Animating responsive characters with dynamic constraints in near-

unactuated coordinates

Yuting

Ye, C. Karen Liu

Combines kinematic motion with dynamic reactions that are constrained to minimize interference with muscles essential to the base motion.

To accomplish this, the system does the following:

Projects motions Slide27

Animating responsive characters… (cont)

Specifically, the system does the following:

For each frame of animation, the actuator torques required to drive the motion are computed

Principle Component Analysis (PCA) is performed on all frames of all animations.

Eigenvectors with the

lowest

8

Eigenvalues

are collected, these form the basis for dynamic reactions.

The biped’s dynamics are represented using 8 DOF, each being a scalar multiple of one of these vectors.

For each frame of animation the forward dynamics of the system are projected onto this basis (J)

f =

Mx

’’ +…

Jf

= (

JMJ

t

)(

Jx

’’) Jtq’’ = x’’Slide28

Animating responsive characters… (cont)

Due to the

orthogonality

guarantees of PCA, these Components act in the null space of the most significant Components.

They correspond to motions requiring little or no muscle action

Hence the term “

unactuated

”

Furthermore the composite effect of these components tend to cancel each other out with respect to side effects at the primary, well actuated DOF.Slide29

Animating responsive characters… (cont)

[video]Slide30

Animating responsive characters… (cont)

Extremely efficient

Fairly convincing

Characters display motion consistent with their style

Primary issues is that for relatively strong perturbations the response is not convincing – you would expect perturbation to effect the walk cycle as a whole, unfortunately the problem statement forbids this.

Changing the problem statement would invalidate this technique.Slide31

Simulating Biped Behaviors from Human Motion Data

Kwang Won, Sok

Manmyung

Kim,

Jehee

Lee

ACM Transactions on Graphics, July 2007.

Uses machine learning to construct physical controllers that can emulate motion capture data while maintaining balance

Controllers operate on an animator specified biped.

The system behaves best when the virtual biped is similar to captured subject.Slide32

Simulating Biped Behaviors … (cont)

Animators construct a finite state machine that describes potential transitions between animations.

Further controllers are generated and trained for each transition

All animations can transition to the balance/ recovery states.Slide33

Controllers consist of parameterized PD servos.

Training consists of finding feedback/time varying parameterization for the servos using a numerical optimization technique with the objective function of minimizing error between the motion capture and the simulated character.

The optimization problem is highly nonlinear

Simulating Biped Behaviors … (cont)Slide34

Optimization strategy:

Initialize solution to {0}

Scatter random points about the current solution

Optimize each point using a gradient descent technique

Select best point(s) and repeat

Slow, brute force method.

Given optimization landscape, faster search methods such as Conjugate Gradients and Quasi Newton fail to converge, so the others use a downhill simplex descent

In the second phase of learning (transitions) the optimization parameterizations consist of a linear combination of existing controllers

Speeds up the optimization considerably

Maintains style of source animations

Key takeaway.

Simulating Biped Behaviors … (cont)Slide35

Key Takeaways

PD servo based physical controllers with very few biped/locomotion specific heuristics are feasible

Blending controllers trained on animation with a specific style seems yield new controllers that maintain this style.

Effectiveness demonstrated for transitions, will blending work with more general extrapolations?

Simulating Biped Behaviors … (cont)Slide36

Interactive Simulation of Stylized 

Human LocomotionMarco da

 Silva, Yeuhi Abe, Jovan Popović - SIGGRAPH 08

Animating natural human motion in dynamic environments is difficult for various reasons, including complex geometric and physical interactions.

Simulation combined with physical controllers has been demonstrated to provide an automatic solution to parts of this problem by the robotics community.

In the field of video game animation, we require style and personality in addition to competent locomotion.Slide37

Describes the systematic synthesis of controllers that can reproduce a range of locomotion styles in interactive simulations.

Given a reference motion that describes the desired style, a control system is derived that can reproduce that style in simulation and in new environments.

Figure 1

Numerical optimization provides a framework for computing a solution that is optimal in the context of any number of prioritized goals.

In this case the goals are balance and style, the optimization technique is quadratic programming.

Simulation of Stylized … (cont)Slide38

A balance strategy is pre-computed for the given style using automated analysis of linear time varying approximations.

By tailoring the balance strategy in this manner, a controller preserves the style better than a more cautious strategy

Simulation of Stylized … (cont)Slide39

The Style feedback loop tracks individual joint angles to compute the accelerations needed to preserve the given style.

Reference motions guide both the style and balance feedback.

The style feedback aims to preserve the nuances of the motion

The balance feedback seeks to adapt the motion of three balance critical segments

The control algorithm computes a final set of forces by maintaining a desired tradeoff between the balance and style feedback.

Simulation of Stylized … (cont)Slide40

Dynamic balance is a hard problem

Many

dof

-> infinite solutions

Numerically unstable -> most states invalid

Valid states may be balanced, but awkward

i.e. if you have a tendency to lean to the left, cranking your head to the right may help you to balance but it is not the most natural way to address the issue.

Simulation of Stylized … (cont)Slide41

To mitigate these problems, the authors:

Project the higher detail (60 DOF) biped onto a low detail (9 DOF, 6 Actuator DOF) model

Use a piecewise linear model to approximate the non-linear dynamics of the reduced detail model.

i.e

F=MA, where M is a constant matrix.

Use a Linear Quadratic Regulator (LQR) that tracks the animation data

Assumes that poses are dynamically stable

Eliminates the need to explicitly search for

any

dynamically stable state.

Simulation of Stylized … (cont)Slide42

The 3 link model’s root is the contact foot and it has two 3 DOF actuators acting at the hips

One for the torso

One for the swing leg

The linearalized dynamic equations are

precomputed

, rolled together with the equations necessary for the LQR, factored and stored for each time step.

This amounts to about 20k per second of animation

The full character would require about 2 meg per second.

Aside: Is 60hz necessary?

Simulation of Stylized … (cont)Slide43

[Video]

Interactive Simulation of Stylized 

Human Locomotion

Marco 

da

 Silva, Yeuhi Abe, Jovan Popović - SIGGRAPH 08Slide44

The results are promising:

Maintains lifelike human motion in dynamic environments, which is difficult to accomplish with kinematics or dynamics aloneTransforms a single recorded motion, valid for one environment only, into a general purpose action that can be used in many other settings or even composed with other actions to create versatile characters.

Performance reasonable in worst case, plenty of room for optimizations in normal situations.

Simulation of Stylized … (cont)Slide45

Combines blended animation with the results of a physical simulation

6 DOF springs are attached to the root of the dynamic model to keep it from falling over.

Dynamo: dynamic, data-driven character control with adjustable balance

ACM Siggraph Video Game Symposium

Pawel

Wrotek

,

Odest

Chadwicke

Jenkins, Morgan McGuire Slide46

While not mentioned explicitly by the authors, blending in world space helps preserve the momentum characteristics of the source animations, thus making the result more physically plausible (if the source animations is physically plausible)

Dynamo: dynamic, data-driven character control with adjustable balance

ACM Siggraph Video Game Symposium

Pawel

Wrotek

,

Odest

Chadwicke

Jenkins, Morgan McGuire Slide47

The reason for the improved preservation of momentum in world space is the fact that the Hessian describing the system dynamics in world space coordinates varies less across poses than the corresponding Hessian describing the system dynamics in joint space.

This insight is arguably the primary take-away from the paper.

A natural corollary is that physical controllers will usually be more robust when operating in world space.

The authors point out this observation in the diagram below.

Dynamo: dynamic, data-driven character control with adjustable balance

ACM Siggraph Video Game Symposium

Pawel

Wrotek

,

Odest

Chadwicke

Jenkins, Morgan McGuire Slide48

Animating reactive motion using Momentum-based Inverse Kinematics

Taku Komura, Edmond S. L. Ho and Rynson

W. H. Lau

Virtual Worlds 2005Slide49

Animating reactive motion … (cont)

System designed for dynamic reactions that allow for recovery.

Intended to bridge the gap between Ragdolls and layered hit reactions for mild impacts that do not affect the characters momentum or trajectory

Animations with different stepping strategies form the basis for reactions

Foot placement of animations is modified counteract the characters momentum.

This balance strategy is basis for

Asimo’s

control system

The ideal foot placement point is called the Zero Moment PointSlide50

Animating reactive motion … (cont)

Momentum Based IK consists of:

Constraining a kinematic motion to conserve momentum each frame

This is achieved by running a physical simulation in which only the non-essential DOF have finite inertia.

Initial DOF accelerations are computed using backwards differencing between the prior state and the animation sample.

Playing back the new, modified animation.

System must be able to maintain velocity/momentum, along with state across framesSlide51

Animating reactive motion … (cont)

Seems quite effective in theory

Unfortunately I was unable to find a video

Fits in nicely with existing systems

Performance cost is low

Not too far from what some people do currentlySlide52

Interactive Dynamic Response for Games

Victor B. Zordan, Adriano Macchietto, Jose Medina,

Maarc

Soriano, Chun-Chih Wu, I3D 07Slide53

Assumes many hit reactions (paper cited 500 in their tests)

Finding the appropriate reaction becomes a bottleneckUses an optimized search tree based on a Support Vector Machine (SVM)

Quality of SVM is highly dependent on the DOF used in classification

Tried many combinations

Optimal set of DOF was 20 in total:

Momentum (linear and angular) at various time steps surrounding the impact

Center of mass at same time steps

Orientation at time of impact

Once a good SVM is constructed, real time search is feasible for a large number of motions

Time to find animation was cited as ~2 ms

vs

2 seconds prior to the SVM.

Interactive Dynamic Response … (cont)Slide54

[video]

Interactive Dynamic Response … (cont)Slide55

Anticipation from Example

Victor Zordan, Chun-Chih Wu, et al.

Time

q(t)

Impact

Reaction Animation

Blend

Find Reaction

Simulation

Detect Impact

Defensive MotionSlide56

Simple Steps for Simply Stepping

Victor Zordan, Chun-Chih Wu, et al.Visual Computing 2008

“Inspired” by the paper Animating reactive motion using Momentum-based Inverse Kinematics

Uses Momentum

based IK to drop animation

Uses principles of Dynamic Balance/Zero moment point to synthesize steps from exampleSlide57

Wrap: Research, Animation and Physics

Figure 3: When shipping a game, elegance and simplicity occasionally slip.

When publishing a paper, elegance and simplicity occasionally slip.

There is a lot of great research and many ideas with strong potential.

When evaluating ideas, it is important to remember that the whole is not always greater than the sum of its parts.Slide58

Characters that preserve the personality and style of their animations

, seamlessly interact with a physically simulated world and take orders from the gamepad of abusive players and confused AIs.

Just about every successful entertainment medium features compelling characters – personality and style are essential.

Animators are often better at creating compelling characters than random algorithms/equations.

In games we have Animators (hopefully) and many algorithms/equations.

Presenting

optimal

relevant algorithms that preserve the personality and style of characters/animators is the focus of this lecture.Slide59

Interactivity defines a video game.

A non-interactive game is a movie.Physics is a powerful tool that can enable Interactivity through simulation.There are other ways but

they suck

They are:

BEYOND THE SCOPE

OF THIS LECTURE

Characters that preserve the personality and style of their animations,

seamlessly interact with a physically simulated world

and take orders from the gamepad of abusive players and confused AIs.Slide60

Characters that preserve the personality and style of their animations, seamlessly interact with a physically simulated world and

take orders from the gamepad of abusive players and confused AIs.

I found two papers addressing this concern

Ideas and or results are depressing

Not worth mentioning

They are:

BEYOND THE SCOPE

OF THIS LECTURESlide61

Questions & Answers

Figure 1: Physical feasibility is one of many challenges faced by otherwise content virtual people.Slide62

Past End of File.

You should not be here.

Please shut down current application.

Reboot if necessary.Slide63

Problem: Game is Interactive.

Animation Database is not.

Modeling the large space of animations that may be required at any moment due to open ended nature of player actions is non-trivial

Rule based controllers handle this via animation selection guided by heuristics.

i.e. pre-authored decision tree populated with animation clips.

Many problems and failure cases

Still the basis for many animation systems

Apparently academia sees this as a problem - over the last five years, the amount of research dedicated to this problem has grown exponentially.Slide64

The Arsenal

Meta techniques

Generalizing from examples

Interpolate and/or extrapolate from source animations

Weighted blend of source (convex interpolation)

Transform source to a new basis using direct techniques (i.e. PCA)

Use source to train/derive more complex models.

Splice to micro motion snippets and transition rapidly

Synthesis

Physical Simulation

Rag dolls

Secondary dynamics

Kinematic and/or Dynamic Constraints

Inverse Kinematics

“Physics Filter”

Physically based controllers

General Human biometric data

The best solutions tend to be hybrid implementations of these techniques, as each has its limitations and strengths.

Hence the term “Arsenal”: Use, understand, build, augment, combine, patentSlide65

Generation and Visualization of Emotional States in Virtual Characters

Diana Arellano , Javier

Varona

, Francisco

Perales

CASA 08

The aim of this paper is to model characters that have PERSONALITY, feel EMOTIONS and can MANIFEST EMOTIONAL STATES

The motive is to create unique, real, distinguishable individuals depending on their

personalities and emotional

states