Motion Capture Markers Research Articles

Data-driven modeling of skin and muscle deformation

In this paper, we present a data-driven technique for synthesizing skin deformation from skeletal motion. We first create a database of dynamic skin deformations by recording the motion of the surface of the skin with a very large set of motion capture markers. We then build a statistical model of the deformations by dividing them into two parts: static and dynamic. Static deformations are modeled as a function of pose. Dynamic deformations are caused by the actions of the muscles as they move the joints and the inertia of muscles and fat. We approximate these effects by fitting a set of dynamic equations to the pre-recorded data. We demonstrate the viability of this approach by generating skin deformations from the skeletal motion of an actor. We compare the generated animation both to synchronized video of the actor and to ground truth animation created directly from the large marker set.

Influence of Gender, Power, and Hand Position on Pelvic Motion during Seated Cycling

An understanding of normal pelvic motion during seated cycling is relevant to saddle design and bicycle fitting. In this study, we investigated the effects of gender, power, and hand position on pelvic motion throughout a pedal stroke. We also investigated whether anthropometric factors could explain any interindividual differences observed. Twelve experienced male and 14 experienced female cyclists participated. Each subject was custom fitted to a stationary bicycle and then rode the bicycle at three power outputs (100, 150, and 200 W), with their hands in the tops and drops position. The kinematics of a triad of motion-capture markers, located on posterior pelvic landmarks, were used to characterize pelvic motion. The largest angular excursions were observed in the nonsagittal planes, with the pelvis rotating internally (approximately 3 degrees ) and rolling laterally ( approximately 2 degrees ) toward the downstroke. These pelvic rotations caused the hip on the downstroke side to translate anteriorly and inferiorly. Compared with males, females exhibited greater average anterior pelvic tilt in the drops hand position (males: 21 +/- 3 degrees ; females: 24 +/- 4 degrees ; P = 0.036). Interindividual differences in pelvic motion could not be independently explained by measures of ischial tuberosity width or hamstring flexibility. However, average anterior pelvic tilt was negatively correlated with lumbar flexibility among the males (r = 0.75; P = 0.024), suggesting that this may be an important factor to consider in bicycle fitting. We observed substantial pelvic motion during seated cycling, with experienced female road cyclists exhibiting greater average anterior tilt than their male counterparts. Pelvic motion seems to arise naturally during seated cycling and should be considered when designing saddles and establishing bicycle-fitting procedures.

Online Motion Capture Marker Labeling for Multiple Interacting Articulated Targets

Abstract In this paper, we propose an online motion capture marker labeling approach for multiple interacting articulated targets. Given hundreds of unlabeled motion capture markers from multiple articulated targets that are interacting each other, our approach automatically labels these markers frame by frame, by fitting rigid bodies and exploiting trained structure and motion models. Advantages of our approach include: 1) our method is an online algorithm, which requires no user interaction once the algorithm starts. 2) Our method is more robust than traditional the closest point‐based approaches by automatically imposing the structure and motion models. 3) Due to the use of the structure model which encodes the rigidity of each articulated body of captured targets, our method can recover missing markers robustly. Our approach is efficient and particularly suited for online computer animation and video game applications.

Identification of passive elastic joint moment–angle relationships in the lower extremity

The purpose of this study was to develop a method for identifying subject-specific passive elastic joint moment–angle relationships in the lower extremity, which could subsequently be used to estimate passive contributions to joint kinetics during gait. Twenty healthy young adults participated in the study. Subjects were positioned side-lying with their dominant limb supported on a table via low-friction carts. A physical therapist slowly manipulated the limb through full sagittal hip, knee, and ankle ranges of motion using two hand-held 3D load cells. Lower extremity kinematics, measured with a passive marker motion capture system, and load cell readings were used to compute joint angles and associated passive joint moments. We formulated a passive joint moment–angle model that included eight exponential functions to account for forces generated via the passive stretch of uni-articular structures and bi-articular muscles. Model parameters were estimated for individual subjects by minimizing the sum of squared errors between model predicted and experimentally measured moments. The model predictions closely replicated measured joint moments with average root-mean-squared errors of 2.5, 1.4, and 0.7 Nm about the hip, knee, and ankle respectively. We show that the models can be coupled with gait kinematics to estimate passive joint moments during walking. Passive hip moments were substantial from terminal stance through initial swing, with energy being stored as the hip extended and subsequently returned during pre- and initial swing. We conclude that the proposed methodology could provide quantitative insights into the potentially important role that passive mechanisms play in both normal and abnormal gait.

Minimizing errors during in vitro testing of multisegmental spine specimens: Considerations for component selection and kinematic measurement

Apparatus-induced artifacts may invalidate standard spine testing protocols. Kinematic measurements may be compromised by the configuration of motion capture equipment. This study has determined: (1) the influence of machine design (component friction) on in vitro spinal kinetics; (2) the sensitivity of kinematic measurements to variations in the placement of motion capture markers. A spinal loading simulator has been developed to dynamically apply pure bending moments (three axes) with or without a simultaneous compressive preload. Two linear slider types with different friction coefficients, one with caged ball bearings and one with high-precision roller bearings on rails, were mounted and specimen response compared in sequential tests. Three different optoelectronic marker cluster configurations were mounted on the specimen and motion data was captured simultaneously from all clusters during testing. A polymer tube with a uniform bending stiffness approximately equivalent to a polysegmental lumbar spine specimen was selected to allow reproducible behavior over multiple tests. The selection of sliders for linear degrees of freedom had a marked influence on parasitic shear forces. Higher shear forces were recorded with the caged-bearing design than with the high-precision rollers and consequently a higher moment was required to achieve a given rotation. Kinematic accuracy varied with each marker configuration, but in general higher accuracy was achieved with larger marker spacings and situations where markers moved predominantly parallel to the camera's imaging plane. Relatively common alternatives in the mechanical components used in an apparatus for in vitro spine testing can have a significant influence on the measured kinematic and kinetics. Low-magnitude parasitic shear forces due to friction in sliders induces a linearly increasing moment along the length of the specimen, precluding the ideal of pure moment application. This effect is compounded in polysegmental specimens. Kinematic measurements are highly sensitive to marker design and placement, despite equivalent absolute precision of individual marker measurements, however marker configurations can be designed to minimize errors related to spatial distribution and system bias.

Estimation of missing markers in human motion capture

Motion capture is a prevalent technique for capturing and analyzing human articulations. A common problem encountered in motion capture is that some marker positions are often missing due to occlusions or ambiguities. Most methods for completing missing markers may quickly become ineffective and produce unsatisfactory results when a significant portion of the markers are missing for extended periods of time. We propose a data-driven, piecewise linear modeling approach to missing marker estimation that is especially beneficial in this scenario. We model motion sequences of a training set with a hierarchy of low-dimensional local linear models characterized by the principal components. For a new sequence with missing markers, we use a pre-trained classifier to identify the most appropriate local linear model for each frame and then recover the missing markers by finding the least squares solutions based on the available marker positions and the principal components of the associated model. Our experimental results demonstrate that our method is efficient in recovering the full-body motion and is robust to heterogeneous motion data.

Estimation of missing markers in human motion capture

Estimation of missing markers in human motion capture

Automatic determination of facial muscle activations from sparse motion capture marker data

We built an anatomically accurate model of facial musculature, passive tissue and underlying skeletal structure using volumetric data acquired from a living male subject. The tissues are endowed with a highly nonlinear constitutive model including controllable anisotropic muscle activations based on fiber directions. Detailed models of this sort can be difficult to animate requiring complex coordinated stimulation of the underlying musculature. We propose a solution to this problem automatically determining muscle activations that track a sparse set of surface landmarks, e.g. acquired from motion capture marker data. Since the resulting animation is obtained via a three dimensional nonlinear finite element method, we obtain visually plausible and anatomically correct deformations with spatial and temporal coherence that provides robustness against outliers in the motion capture data. Moreover, the obtained muscle activations can be used in a robust simulation framework including contact and collision of the face with external objects.

SCAPE

We introduce the SCAPE method (Shape Completion and Animation for PEople)---a data-driven method for building a human shape model that spans variation in both subject shape and pose. The method is based on a representation that incorporates both articulated and non-rigid deformations. We learn a pose deformation model that derives the non-rigid surface deformation as a function of the pose of the articulated skeleton. We also learn a separate model of variation based on body shape. Our two models can be combined to produce 3D surface models with realistic muscle deformation for different people in different poses, when neither appear in the training set. We show how the model can be used for shape completion --- generating a complete surface mesh given a limited set of markers specifying the target shape. We present applications of shape completion to partial view completion and motion capture animation. In particular, our method is capable of constructing a high-quality animated surface model of a moving person, with realistic muscle deformation, using just a single static scan and a marker motion capture sequence of the person.

Non-Rigid Modeling of Body Segments for Improved Skeletal Motion Estimation

A necessary requirement for many musculoskeletal modeling tasks is an estimation of skeletal motion from observations of the surface of a body segment. The skeletal motion may be used directly for inverse kinematic calculations or as an observation sequence for forward dynamic simulations. This paper describes a fundamentally new approach to human motion capture for biomechanical analysis. Techniques for generating three-dimensional models of human skeletal elements from magnetic resonance imaging data are described, along with a methodology for corresponding these highresolution internal models to externally observable features. A system for generating dynamic visualizations of these skeletal models from retro-reflective, skin-mounted marker motion capture data is also developed. Next, a set of techniques for estimating body segment shape and pose without the need for retro-reflective markers, from single and multiple, calibrated and un-calibrated cameras is developed. Example results from both synthetic and actual data sequences are presented.