Recent advances in 3D imaging technologies give rise to databases of human shapes, from which statistical shape models can be built. These statistical models represent prior knowledge of the human shape and enable us to solve shape reconstruction problems from partial information. Generating human shape from traditional anthropometric measurements is such a problem, since these 1D measurements encode 3D shape information. Combined with a statistical shape model, these easy-to-obtain measurements can be leveraged to create 3D human shapes. However, existing methods limit the creation of the shapes to the space spanned by the database and thus require a large amount of training data. In this paper, we introduce a technique that extrapolates the statistically inferred shape to fit the measurement data using non-linear optimization. This method ensures that the generated shape is both human-like and satisfies the measurement conditions. We demonstrate the effectiveness of the method and compare it to existing approaches through extensive experiments, using both synthetic data and real human measurements.
@cite_0 @cite_23 compute a new triangular mesh based on a given set of measurements. Starting from a parameterized database of human bodies in similar poses, the approach performs Principal Component Analysis (PCA) of the data. This yields one PCA weight for each training shape. The training database is used to learn a linear mapping from the set of measurements measured on the training data to the PCA space. This mapping is called . Feature analysis can be used to compute a new PCA weight based on a new set of measurements, and the learned PCA model allows to compute a new triangular mesh from this PCA weight. @cite_27 perform feature analysis on a database of human shapes consisting of feature patches to find a new model consisting of smooth patches.
@cite_0 @cite_27 @cite_23
2099011563 2048454024 2154332168
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. This paper presents an exemplar-based method to provide intuitive way for users to generate 3D human body shape from semantic parameters. In our approach, human models and their semantic parameters are correlated as a single linear system of equations. When users input a new set of semantic parameters, a new 3D human body will be synthesized from the exemplar human bodies in the database. This approach involves simpler computation compared to non-linear methods while maintaining quality outputs. A semantic parametric design in interactive speed can be implemented easily. Furthermore, a new method is developed to quickly predict whether the parameter values is reasonable or not, with the training models in the human body database. The reconstructed human bodies in this way will all have the same topology (i.e., mesh connectivity), which facilitates the freeform design automation of human-centric products. In this paper, we demonstrate a system for synthesizing high-resolution, realistic 3D human body shapes according to user-specified anthropometric parameters. We begin with a corpus of whole-body 3D laser range scans of 250 different people. For each scan, we warp a common template mesh to fit each scanned shape, thereby creating a one-to-one vertex correspondence between each of the example body shapes. Once we have a common surface representation for each example, we then use principal component analysis to reduce the data storage requirements. The final step is to relate the variation of body shape with concrete parameters, such as body circumferences, point-to-point measurements, etc. These parameters can then be used as "sliders" to synthesize new individuals with the required attributes, or to edit the attributes of scanned individuals.