Skeleton-based 3D Metaball Human Model
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Transcript Skeleton-based 3D Metaball Human Model
3D Skeleton-based Human
Modeling with Metaballs
18 April 2008
Donghun Kim
Robot Vision Lab
Contents
Motivation
What is the Metaball?
How can we visualize Metaballs?
3D Skeleton Human Model
Human model using Metaballs
Examples
Future Works
Motivation
Motion Analysis of Non-rigid Object
Motion Analysis of Articulated Object
Model based Pose Estimation and Tracking
3D Mesh Data by Visual Hall
3D Skeleton( Curve Skeleton by Thinning, DT,
Geometric or General Fields) - on going
3D Skeleton-based Motion tracking
Metaball Human Model for shape recovery
Motion Analysis by Skeleton-based Motion
and Shape Info. (i.e. Manifold learning)
Implicit Surfaces
Implicit Surfaces: Surfaces which are
contours(isosurface) through some
scalar field in 3D
Different Field function: Metaball, Soft
Objects, Blobbies
What are isosurfaces?
Consider a function f(x,y,z) which define a
scalar field in 3D space.
Isosurface S is set of points for which
f(x,y,z) = const.
It can be thought as an Implicit function
relating x,y and z -> so called implicit
surface sometimes
Metaballs
x: a point in 3d space
A particularly interesting case
Use implicit equation of the form
N
i 1
ri
x pi
2
1
pi : center position of
metaballs
ri : Metaball’s own density
field
Gradient can be computed directly
N
2 ri
i 1
x pi
f
2
N: num. of metaballs
2
4
( x pi )
Soft/blobby objects that blend into each other
Field Functions
Blobby Molecules
r is distance of a point in
space to a particular
control point
br2
D(r) ae
Metaballs
3r2
a(1 2 )
3a b r
D(r ) (1 ) 2
b
2
0
if 0 r
b
3
b
r b
3
if b r
if
Soft Objects
4r 6 17r 4 22r 2
D(r ) a (1 9b 6 9b 4 9b 2 )
0
Comparison of Field Functions
< Figure from [4]>
Examples
< Images from [4]>
Metaballs are cool!
Marching Cubes
Well-known Method for scalr field
polygonizataion
Sample f(x,y,z) on a cubic lattice
For each cubic cell
Estimate where isosurface inetersects cell
edges by linear interpolation
Tessellate depending on values of f() at
cell vertices
Marching Cubes
Algorithm for creating a polygonal surface
representation of an isosurface of a 3D scalar
field
Combine simplicity with high speed because
of using lookup tables
Application
Reconstruction of a surface from volumetric
datasets
Creating a 3D contour of a mathematical scalar
field
Marching Cubes
Each vertex can be either “inside” or “outside”
For each cube cell there are 256 ways for
isosurface to intersect it can be simplified
down to 15 unique
Marching Cubes
Example
cubeindex = 0; if (grid.val[0] < isolevel) cubeindex
|= 1; if (grid.val[1] < isolevel) cubeindex |= 2; if
(grid.val[2] < isolevel) cubeindex |= 4; if (grid.val[3
< isolevel) cubeindex |= 8; if (grid.val[4] < isolevel)
cubeindex |= 16; if (grid.val[5] < isolevel)
cubeindex |= 32; if (grid.val[6] < isolevel)
cubeindex |= 64; if (grid.val[7] < isolevel)
cubeindex |= 128;
< Images from [5]>
P = P1 + (isovalue - V1) (P2 - P1) / (V2 - V1)
Marching Cubes
Sampling Grid Resolution
Smoothness and Processing time to display
< Images from [5]>
Examples of Marching Cubes
• More Information about Marching Cubes is in [3].
3D Skeleton Human Model
Structure( 15 nodes, 14 metaballs)
7
12
8
3
2
6 11
1
0
4
9
13
5
10
14
15
Human Model using Metaballs
Head(1-2-7)
Human Model using Metaballs
Chest(0-1-2-3-6)
Human Model using Metaballs
Left Arm(1-2-3-8-12)
Human Model using Metaballs
Right Arm(1-2-5-11-15)
Human Model using Metaballs
Left Hip & Thigh(0-4-9)
Human Model using Metaballs
Right Hip & Thigh(0-5-10)
Human Model using Metaballs
Left Leg(4-9-13)
Human Model using Metaballs
Right leg(5-10-14)
Human Model using Metaballs
Result (Polygon Surface)
Human Model using Metaballs
Result (Filled surface)
Programming
3D cloud data and mesh data from Visual Hall(using
EPVH lib) as the input of 3D skeleton
3D Human Model Tool based on Jonney’s 3D Human
Model Frame (using Ellipsoid, Cylinder, Sphere)
OpenGL and FLTK GUI based tool
Functions
3D reconstruction by Visual hall
Obtain Multiple Camera-view point images in OpenGL Environment
Calculate Camera matrix by transforming the OpenGL representation to
Physical camera representation
Parameterized 3D Skeleton Human Model
3D Skeleton-based Metaball Model
Simple model size option (Height considering body ratio, fat-thin
option)
Image Saving with OpenCV in OpenGL
Modeling Tool
Made by Dave Kim and Thanks to Jonney
Modeling Tool
Made by Dave Kim and Thanks to Jonney
Data Comparison
Multi-View Silhouette Images of
Different Human Model
Ellipsoid, Cylinder and Sphere vs. Metaballs
3D cloud points and triangle meshes by
Visual hall
Example (I)
Example (I)
Example (II)
Metaball Human Model
Example (II)
Result by Visual Hall
Future Work
3D Curve skeleton Algorithm[2]
DT (fast) + General Field (robust)
3D skeleton human model fitting to
Curve Skeleton -> Pose Estimation
Shape recovery using the information
from the skeletonization
Motion Analysis
Reference
4.
Clement Menier, Bruno Raffin, “3D Skeleton-based Pose
Recovery,” The 3rd international Symposimum on 3D Data
Processing, Visualization and Transmission, 2006
Nicu D. Cormea etc at al., “Curve-Skeleton Application,” IEEE
Visualization 2005
T. S. Newman, H. Yi, “A survey of the marching cubes
algorithm,” Computers and Graphics, 2006
Implicit Surfaces written by Paul Bourke, June 1997,
5.
Polygonising A Scalar Field written by Paul Bourke, May 1994,
1.
2.
3.
http://local.wasp.uwa.edu.au/~pbourke/modelling_rendering/i
mplicitsurf/
http://local.wasp.uwa.edu.au/~pbourke/geometry/polygonise