Londra

Londra, Shape and Animation Modelling of a

3D Dog FaceAndres Adolfo Navarro Newball

Prof. Geoff Wyvill, Dr. Brendan McCane (University of Otago)Dr. Edmond Prakash (Manchester Metropolitan University)

Several human facial animation models developed in the last 30 years.

Less attention given to animal facial models.

Animal facial anatomical features are usually humanised, oversimplified, cartoonised or ignored.

Motivation

Goal

We aim to create a virtual dog head capable of displaying facial expressions.

The expressive dog Londra

Project Overview

Successfully synthesises dog facial expressions such as anger, affection, attention, fear, happiness, yawning and smelling without displaying anthropomorphic features.

A preliminary validation suggests that most expressions were recognised consistently.

Londra

A pure bottom up form of the layered approach for the bone, muscle, complementary, skin and fur layers.

Tabulated Sphere Subsets to provide a fast way to approximate collisions between objects with constrained motion.

Key contributions

Anatomical differences between humans and dogs.

Lack of anatomical and biometric information.

Scarcity of 3D digitised data from dogs.

Challenges

Charles Darwin Dog video observation Artificial pets Validation:

◦ Quantitative◦ Qualitative◦ Performance◦ Beyond

Methodology

Darwin, C. (1890). The Expression of the Emotions in Man and Animals. London:Francis Darwin ed.

Dog expressions observations

Open nostril

Close nostril

0 1,2

37

2,2

72

3,5

05

4,6

86

5,8

T

Smell

11 Dog videos analysed which complement Darwin’s descriptions

0 0,7

71

6,5

63

7,1

29

7,7

91

8,4

65

Raise ears

Twist head right

Untwist head

T

Attention

T

0 1,4

88

1,7

2,0

28

2,5

74

3,0

64

27

,04

8

Mouth

Lips

Ears

Tongue

Anger

Darwin’s anger

Anger

0.331000000000002

0.09100000000000020.174

0.2290.1730.199

0.694000000000001

0.496000000000001

0.299

0.0759999999999999

0.754000000000003

0.0760000000000005

0.885000000000001

0.7440000000000030.7570000000000040.788

3.217

1.7

0.0940000000000002

0.767000000000003

0.1900000000000020.221

0.453

0.846000000000001

0.283

2.277

Background

Description of expressions

AU Name AU Name

123456789101112

Upper lip raiserLower lip depressorNostril dilatorMouth corner moverLower eyelid depressorUpper eyelid depressorEar advancerEar lowererJaw raiserTongue retractor, drawerTongue depressorEyeball mover - rotator

131415161718192021222324

Eyeball retractorEyeball vertical mover – rotatorHead raiserHead lateral moverHead rotatorTail raiserTail extenderTail lateral moverBody raiserLeft paw raiserRight paw raiserHair raiser

Anger:Tail is erect and rigid 18 (1) +

Ears are directed forward 7 (1) +

Upper lip is raised 1 (1) + 4 (0.5)

Architecture

Setting up

DFACSAU Name AU Name

123456789101112


131415161718192021222324


Processing

DFACSAU Name AU Name

123456789101112


131415161718192021222324


F

Output

Import skull

Import nose Place deformersCreate tongue

Place eyes

Place muscles

Smoothed skinwith lips

TSSs for:-Jaw motion-Muscle and skin interaction-Tongue motion

Euler integration

Deformed skinDeformed tongueDeformed noseTransformed eyes

Render animated expression

Render with OpengGL

Render with Blender

Bottom up approach

Bone layer Muscle layer

Temporalis

Zygomaticus

FrontalisLevator Oculis

Masseter

Digastricus

Orbicularis OrisMentalis

LevatorAuricularisrostrales

Orbicularis OculisAuricularisdorsales

Barr, A. H. (1981). Superquadrics and Angle-Preserving Transformations. IEEE Comput.Graph. Appl., 1 (1), 11–23.

α DistEyes

P

D

B)A)

X ≤ X0 X ≥ X0

Sn (X0, Y0, Z0)Z

Cornea

Sclera

Iris

Pupil

Back Middle Tip

C)

D) E)

GrooveMT

MTM

D

DistEyes

2sin 1

Complementary layer

A) B) C)

R

R +d

αAxis

D0

D0

D1

D2D) E)

Skin Layer

Skin layer -Deformation

P

P’

R

F

P

P’

R

F Nm

Ns

D

R F

Muller, M., Heidelberger, B., Teschner, M., and Gross, M. (2005). Meshless deformationsbased on shape matching. In SIGGRAPH ’05: ACM SIGGRAPH 2005 Papers,New York, NY, USA, 471–478. ACM.

King, S. A. (2001). A facial model and animation techniques for animated speech. Ph.D. thesis, The Ohio State University.

Kobbelt, L. (2000). √3-subdivision. In SIGGRAPH ’00: Proceedings of the 27th annualconference on Computer graphics and interactive techniques, New York, NY, USA,103–112. ACM Press/Addison-Wesley Publishing Co.

Tabulated Spheres Subsets

Definition

Msa Ms

b

a b

Line of motion, 1 DOF Ms= Approximation with spheres.n = number of spheres in a = 9m = number of spheres in b = 18 n X m = 9 X 18 = 162 tests

Msc= Colliding spheres subset.n = 3m = 7n X m = 21 tests

Msca

a b

Mscb

Mssa

a b

Mss= Minimal subset.n = 3m = 3n X m = 9 testsTSSAB = Msa X Msb = compares non redundant spheres of the same colour against spheres of the same colour only.TTSS = 3 tests

Mssb

22 tests time step

Jaw motion

Muscle skin interaction

WANG W., WANG J., KIM M.-S.: An algebraic conditionfor the separation of two ellipsoids. Comput. Aided Geom.Des. 18, 6 (2001), 531–539.

The use of a more freely moving object. A flexible object interacting with more than

one object The use of an object which has been divided

in several sections which need to interact with each other.

Extraordinary cases where some collisions need to be ignored.

Tongue motion

A)

α

β

4α

4α

2α

4β

4β 2

β

B) C)

2 – 5 tests

Expression synthesis

AU13: Raise eyesAU12: Left eyes AU6: Raise ears

AU11: Twist head AU9: Raise head

AU -14: Relax eyesAU -9: Lower head AU9: Raise headAU7: Open mandible AU16: Lick

A)

B)

C)

D)

Original

Anger Attention Affection II Yawn

Smell

Happiness Fear Affection I

Londra’s videos can be downloaded at: http://cic.javerianacali.edu.co/~anavarro/Londra/

Validation

U flow V flow Mixed flowTwo framesA)

A)

B)

C)

Survey

Performance

60 145 245 370 10510

5

10

15

20

25

18.517.2

14.7 13.2 13.7

21.3 20.819.2

17.9 18.2

FPS, SL2

FPS, SL1

Collision tests

FPS

Beyond

We followed a new pure bottom up approach which starts with a skull and does not require a pre-existing facial mesh.

We introduced TSSs, a fast and appropriate method for constrained object interaction.

We validated eight of Londra’s expressions successfully.

Conclusions

Enhancing our bottom up approach by creating an anatomically accurate skull reshaping method in conjunction with zoology. Then, automating muscle placement.

Our TSSs open a full field of research for constrained object interaction. For example, mirrored TSSs.

The Londra model could be expanded to other real or non real non human creatures. And some of the techniques could be used in human systems.

Further work

Some videos

• Blender 1• Blender 2• Blender 3

Londra

Documents

Transcript of Londra