3D modelling of elastic articulated objects and its
parameter determination from image points
H L Pan*
a
, L Shi
b
, Z Min
c
and Y W Dai
a
a
School of Automation, Nanjing University of science and technology, Nanjin g 210094, China
b
Shanghai Berkeley Biology and Technology Limited Compan y, Shanghai 200433, China
c
College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics,
Nanjing 210016, China
Abstract: This paper presents a new model of elastic articulated objects based on revolving conic
surface. The model includes 3D object skeleton and deformable surfaces that can represent the
deformation of human body surfaces. In each limb, surface deformation is represented by adjusting
only one deformation parameter. Then, the 3D deformation parameters are determined by
corresponding 2D image points from a sequence of stereo images. The 3D motion parameters are
estimated based on the skeleton points. The algorithm presented in this paper includes skeleton-
based parameter estimation of motion and parameter determination of deformable surfaces.
Keywords: elastic articulated objects, human body modeling, revolving conic surfaces,
motion estimation
1 INTRODUCTION
Objects in motion analysis are mainly rigid objects,
articulated objects, non-rigid objects and so on. In
computer vision research, motion analysis has been
largely restricted to rigid objects. However, in the real
world, motion of non-rigid objects is the rule.
1,2
The
study of non-rigid motion is receiving increasing
attention from computer vision researchers. This
interest is motivated by applications over a wide
spectrum of areas such as material deformation
studies, medical imaging, biomedical applications,
video conferencing and image compression.
In the past researches, a number of modelling
methods of human deformable bodies have been
proposed. Nahas described how to use B-Spline
surfaces to model lissome movements of body and
face.
3
Pentland introduced a physically correct model
of elastic non-rigid motion.
4
This model is based on
the finite element method. Min applied soft objects to
create muscles,
5
which are classified into three types
according to the primitives of the soft objects: a point
primitive generates an ellipsoid, a set of linked line
segments generate a generalized cylinder, and a
triangular mesh generates a complex shape.
Plankers developed a framework for 3D shape and
motion recovery of articulated deformable objects.
6
Smooth implici t surfaces, known as metaballs, are
attached to an articulated skeleton of the human
body and are arranged in an anatomically-based
approximation. This particular human body model
includes 230 meta balls. Sminchisescu built a human
body model which consists of kinematical ‘skeletons’
of articulated joints controlled by angular joint
parameters,
7
covered by ‘flesh’ built from super-
quadric ellipsoids with additional tapering and
bending parameters. A typical model has around 30
joint parameters, plus eight internal proportion
parameters and nine deformable shape parameters
for each body part. Apuzzo presents a simplified
ellipsoidal meatball model of a limb.
8
Only three
ellipsoidal metaballs are attached to each limb
The MS was accepted for publication on 21 October 2008.
*Corresponding author: Hailang Pan, School of Automation,
Nanjing University of Science and Technology, Nanjing 210094,
China; email: hailangpan@163.com
79
IMAG mpa034
#
RPS 2009 DOI: 10.1179/174313109X373684 The Imaging Science Journal Vol 57
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