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.
Seo and Megnenat-Thalmann @cite_12 represent the human body as a triangular mesh with an associated skeleton model. As with the approach of , this approach reduces the dimensionality of the data using PCA. This yields a set of PCA weights. The approach learns a mapping from the set of measurements measured on the training data to the PCA space using an interpolating radial basis function (RBF) with Gaussian kernel @cite_1 . As in the approach of , this mapping produces a shape based on a new set of measurements. @cite_5 apply the two previously reviewed approaches to a new representation of human body shapes that is posture invariant. Their method simultaneously models body pose and shape.
@cite_5 @cite_1 @cite_12
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We propose a novel geometric framework for analyzing 3D faces, with the specific goals of comparing, matching, and averaging their shapes. Here we represent facial surfaces by radial curves emanating from the nose tips and use elastic shape analysis of these curves to develop a Riemannian framework for analyzing shapes of full facial surfaces. This representation, along with the elastic Riemannian metric, seems natural for measuring facial deformations and is robust to challenges such as large facial expressions (especially those with open mouths), large pose variations, missing parts, and partial occlusions due to glasses, hair, and so on. This framework is shown to be promising from both-empirical and theoretical-perspectives. In terms of the empirical evaluation, our results match or improve upon the state-of-the-art methods on three prominent databases: FRGCv2, GavabDB, and Bosphorus, each posing a different type of challenge. From a theoretical perspective, this framework allows for formal statistical inferences, such as the estimation of missing facial parts using PCA on tangent spaces and computing average shapes. Abstract This paper presents a novel feature based parameterization approach of human bodies from the unorganized cloud points and the parametric design method for generating new models based on the parameterization. The parameterization consists of two phases. First, the semantic feature extraction technique is applied to construct the feature wireframe of a human body from laser scanned 3D unorganized points. Secondly, the symmetric detail mesh surface of the human body is modeled. Gregory patches are utilized to generate G 1 continuous mesh surface interpolating the curves on feature wireframe. After that, a voxel-based algorithm adds details on the smooth G 1 continuous surface by the cloud points. Finally, the mesh surface is adjusted to become symmetric. Compared to other template fitting based approaches, the parameterization approach introduced in this paper is more efficient. The parametric design approach synthesizes parameterized sample models to a new human body according to user input sizing dimensions. It is based on a numerical optimization process. The strategy of choosing samples for synthesis is also introduced. Human bodies according to a wide range of dimensions can be generated by our approach. Different from the mathematical interpolation function based human body synthesis methods, the models generated in our method have the approximation errors minimized. All mannequins constructed by our approach have consistent feature patches, which benefits the design automation of customized clothes around human bodies a lot. We represent human body shape estimation from binary silhouettes or shaded images as a regression problem, and describe a novel method to tackle it using CNNs. Utilizing a parametric body model, we train CNNs to learn a global mapping from the input to shape parameters used to reconstruct the shapes of people, in neutral poses, with the application of garment fitting in mind. This results in an accurate, robust and automatic system, orders of magnitude faster than methods we compare to, enabling interactive applications. In addition, we show how to combine silhouettes from two views to improve prediction over a single view. The method is extensively evaluated on thousands of synthetic shapes and real data and compared to state of-art approaches, clearly outperforming methods based on global fitting and strongly competing with more expensive local fitting based ones. We describe a method to capture disease-specific components in organ shapes. A statistical shape model, constructed by the principal component analysis (PCA) of organ shapes, is used to define the subspace representing inter-subject shape variability. The first PCA is applied to the datasets of healthy organ shapes to define the subspace of normal variability. Then, the datasets of diseased shapes are projected onto the orthogonal complement (OC) of the subspace of normal variability, and the second PCA is applied to the projected datasets to derive the subspace representing the disease-specific variability. To calculate the OC of an n-dimensional subspace, a novel closed-form formulation is developed. Experiments were performed to show that the support vector machine classification in the OC subspace better discriminated healthy and diseased liver shapes using 99 CT data. The effects of the number of training data and the difference in segmentation methods on the classification accuracy were evaluated to clarify the characteristics of the proposed method.