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.
Wang @cite_26 uses a parameterized database of human shapes consisting of to find a new human shape based on a given set of measurements. Feature patches are initialized to smooth patches that are subsequently refined to capture geometric details of the body shape. While this refinement does not maintain the smoothness of the patches, the final results are visually smooth and do not contain realistic localized shape details. The approach finds models in the database with measurements similar to the given set of measurements and computes the new human shape as a linear combination of these models. @cite_21 use a similar approach, but the models are represented using a layer-based representation.
@cite_26 @cite_21
2020466163 1569618975
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. Personalize mannequin design are the basic element in virtual garment simulation and visualization. This paper addresses the problem of mannequin construction from scanned point cloud and regenerates their shapes by inputting variant dimension. Layer-based simplification and parameterization algorithm is presented to preserve the mannequin's features as far as possible. The regenerated models are watertight and used for clothes design.