When designing a product that needs to fit the human shape, designers often use a small set of 3D models, called design models, either in physical or digital form, as representative shapes to cover the shape variabilities of the population for which the products are designed. Until recently, the process of creating these models has been an art involving manual interaction and empirical guesswork. The availability of the 3D anthropometric databases provides an opportunity to create design models optimally. In this paper, we propose a novel way to use 3D anthropometric databases to generate design models that represent a given population for design applications such as the sizing of garments and gear. We generate the representative shapes by solving a covering problem in a parameter space. Well-known techniques in computational geometry are used to solve this problem. We demonstrate the method using examples in designing glasses and helmets.
@cite_15 used the traditional sparse anthropometric measurements to automatically create an apparel sizing system that has good fit. For a fixed set of measurements on the body, a fixed number of sizes, and a fixed percentage of the population that needs to be fitted by the sizing system, they optimize the fit of the sizing system. The fit for a human body is defined by a weighted distance function between the measurements on the body and the measurements used for the sizing system. Optimizing the fit amounts to solving a non-linear optimization system. This system is hard to solve and suboptimal solutions may be obtained. This approach can operate in multi-dimensional spaces. It is not straight forward to obtain design models from the resulting sizing system because computing a design model from a sparse set of measurements is an under constrained problem.
@cite_15
2157100308 2099011563 2131399049 2089695649
A novel approach for the construction of apparel sizing systems is formulated. As a first step to this process, efficient sizing systems are defined based on a mathematical model of garment fit. Nonlinear optimisation techniques are then used to derive a set of possible sizing systems using multidimensional information from anthropometric data. The method is illustrated by developing a sizing system designed for a dress shirt of a military uniform using anthropometric data from the US Army. Results of this analysis show that endogenous size assignment and selection of disaccommodated individuals, together with relaxation of the requirement of a ‘stepwise’ size structure, results in substantial improvements in fit over an existing sizing system. The proposed methodology enables the development of sizing systems that can either increase accommodation of the population, reduce the number of sizes in the system, or improve overall fit in accommodated individuals. We develop a novel method for fitting high-resolution template meshes to detailed human body range scans with sparse 3D markers. We formulate an optimization problem in which the degrees of freedom are an affine transformation at each template vertex. The objective function is a weighted combination of three measures: proximity of transformed vertices to the range data, similarity between neighboring transformations, and proximity of sparse markers at corresponding locations on the template and target surface. We solve for the transformations with a non-linear optimizer, run at two resolutions to speed convergence. We demonstrate reconstruction and consistent parameterization of 250 human body models. With this parameterized set, we explore a variety of applications for human body modeling, including: morphing, texture transfer, statistical analysis of shape, model fitting from sparse markers, feature analysis to modify multiple correlated parameters (such as the weight and height of an individual), and transfer of surface detail and animation controls from a template to fitted models. In this paper, we present a four-step technique for simultaneously estimating a human's anthropometric measurements (up to a scale parameter) and pose from a single uncalibrated image. The user initially selects a set of image points that constitute the projection of selected landmarks. Using this information, along with a priori statistical information about the human body, a set of plausible segment length estimates is produced. In the third step, a set of plausible poses is inferred using a geometric method based on joint limit constraints. In the fourth step, pose and anthropometric measurements are obtained by minimizing an appropriate cost function subject to the associated constraints. The novelty of our approach is the use of anthropometric statistics to constrain the estimation process that allows the simultaneous estimation of both anthropometry and pose. We demonstrate the accuracy, advantages, and limitations of our method for various classes of both synthetic and real input data. We present a novel optimisation framework for the estimation of the multi-body motion segmentation and 3D reconstruction of a set of point trajectories in the presence of missing data. The proposed solution not only assigns the trajectories to the correct motion but it also solves for the 3D location of multi-body shape and it fills the missing entries in the measurement matrix. Such a solution is based on two fundamental principles: each of the multi-body motions is controlled by a set of metric constraints that are given by the specific camera model, and the shape matrix that describes the multi-body 3D shape is generally sparse. We jointly include such constraints in a unique optimisation framework which, starting from an initial segmentation, iteratively enforces these set of constraints in three stages. First, metric constraints are used to estimate the 3D metric shape and to fill the missing entries according to an orthographic camera model. Then, wrongly segmented trajectories are detected by using sparse optimisation of the shape matrix. A final reclassification strategy assigns the detected points to the right motion or discards them as outliers. We provide experiments that show consistent improvements to previous approaches both on synthetic and real data.