- 最后登录
- 2017-9-18
- 注册时间
- 2011-1-12
- 阅读权限
- 90
- 积分
- 12276
![Rank: 7](static/image/common//star_level3.gif) ![Rank: 7](static/image/common//star_level2.gif) ![Rank: 7](static/image/common//star_level1.gif)
- 纳金币
- 5568
- 精华
- 0
|
1 Introduction
Accurate modeling of range of joint motions (joint ROM) is a fundamental
problem of articulated figure animation. The joint ROM
should be carefully designed to avoid an impossible pose, requiring
tedious work because of the complexity and extensiveness of
human joints, especially shoulders and hips. Although many joint
ROM models have been proposed in the field of biomechanics and
graphics, they still have two issues. The first is that a ROMof spherical
joint is defined in a spherical domain. A traditional approach
respectively defines a range of yaw, pitch and roll of joint rotation,
which often causes an unexpected artifact due to the nonlinearity
of the Euler angles. Another method uses a sophisticated parameterization
of 3D rotation [Herda et al. 2005] or a 3D geometrical
model to represent a boundary of joint orientation. These methods,
however, require extra computational cost. The second issue
is that a joint ROM is separately defined for each joint. Previous
models often neglect a strong dependency between adjacent joints;
a shoulder’s joint ROM varies depending on elbow angle for example.
A complex mechanism is therefore required to simulate such
dependency [Herda et al. 2005].
We propose a model to represent ranges of limb motion (limb
ROM). The key idea of limb ROM is to define a space of possible
pose of a limb 1, instead of defining ROM of each joint. A
limb ROM is composed of a valid 3D workspace of the wrist and a
range of swivel angle at arbitrary location in the workspace; “swivel
angle” [Tolani et al. 2000] denotes a rotation angle of the elbow
around an axis with which shoulder and wrist are connected. Our
method has three contributions: 1) our method successfully simulates
a dependency between ROM of a joint and rotation of its
neighbor because a limb ROM model limits movements of multiple
joints simultaneously. 2) A compact limb ROM model is automatically
estimated from a sparse collection of example poses based on
an empirical assumption. 3) Our limb ROM model is well suited
to a real-time inverse kinematics (IK) technique [Tolani et al. 2000]
because our model consists of wrist position and swivel angle, both
of which are the control inputs of the IK solver. Prior to***cuting
the IK solver, an impossible limb pose is efficiently avoided by relocating
the wrist position into the valid workspace and adjusting
swivel angle while fixing the wrist position.
2 Technical approach
2.1 Model cons***ction
A limb ROM model represents a valid range of directions from
shoulder to wrist, and range of swivel angle and distance from
shoulder to wrist along a direction as shown in Figure (a). This
model is cons***cted from a collection of example poses using a
non-parametric regression technique. An example data of the nonparametric
model is composed of wrist position and swivel angle
in a shoulder’s local coordinate system. As a collection of examples
is very sparse in practice, missing data is interpolated from the
measurements. To improve the interpolation accuracy, the dimensionality
of the interpolation problem is reduced on an empirical
assumption: a range of swivel angle changes depending on only
direction from shoulder to wrist without being constrained by a dis-
∗e-mail: tmki@acm.org
1In this paper we explain about only upper limbs.
Swivel
angle
Distance between
shoulder and wrist
Direction from
shoulder to wrist
a) Limb ROM model b) Workspace of right wrist c) Workspace of left ankle
(These bumpy surfaces are caused by simple marching cubes)
tance to wrist. Based on this assumption, example data is mapped
onto a surface of unit sphere on which a direction from shoulder to
wrist is represented by a point. The point on the surface stores a
distance to wrist and a swivel angle of the example. Missing data is
then interpolated using uniform sampling by following four steps:
Sampling points are first distributed uniformly on the spherical surface.
Secondly, minimum and maximum values of swivel angle and
those of distance to wrist are respectively searched within a certain
radius around each sampling point. Thirdly, the searched values are
stored in each sampling point, and points having no value are removed.
Finally, a valid region on the spherical surface is defined by
detecting a closed outer hull which includes all available sampling
points.
2.2 Validation of limb poses
A limb pose is validated using our model in two steps: A direction
to wrist is first validated by checking whether it is mapped within
the valid region on the spherical surface. A distance to wrist and
a swivel angle are then checked whether they are within the range
that is calculated using a k-nearest neighbor interpolation of the
samples.
Our method provides a straightforward solution to synthesize a
possible pose using an IK solver designed for human limbs [Tolani
et al. 2000]. Our model enables a constant-time validation and correction
of wrist position and swivel angle prior to an***cution of
the IK solver, whereas the previous method incorporates a joint
ROM with an optimization framework.
3 Discussion
We created limb ROM models of arms and legs using motion capture
data of a loose-limbed actress. Figure (b) and (c) show an
approximated workspace of right wrist and left ankle, respectively.
These results demonstrate reasonable accuracy of our model. One
major limitation is that our model still requires a large amount
of memory which increases according to the number of sampling
points. Our future work includes an investigation of a more compact,
parametric limb ROM model, which would be accomplished
by using a polynomial approximation technique.
References
Herda, L., Urtasun, R., and Fua, P. 2005. Hierarchical implicit
surface joint limits for human body tracking. Computer Vision
and Image Understanding 99, 2, 189–209.
Tolani, D., Goswami, A., and Badler, N. I. 2000. Real-time inverse
kinematics techniques for anthropomorphic limbs. Graphical
Models 62, 5, 353–388. |
|