3DBODY.TECH 2017 - Paper 17.337

J. Huang et al., "Statistical Learning of Human Body through Feature Wireframe", in Proc. of 3DBODY.TECH 2017 - 8th Int. Conf. and Exh. on 3D Body Scanning and Processing Technologies, Montreal QC, Canada, 11-12 Oct. 2017, pp. 337-346, doi:10.15221/17.337.

Title:

Statistical Learning of Human Body through Feature Wireframe

Authors:

Jida HUANG 1, Tsz-Ho KWOK 2, Chi ZHOU 1

1 Industrial and Systems Engineering, University at Buffalo, SUNY, Buffalo NY, USA;
2 Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal QC, Canada

Abstract:

Statistical learning of human body shape can be used for reconstructing/estimating body shapes from incomplete data, semantic parametric design, modifying images or videos, or simulation. It is applicable in many areas including computer vision & graphics, ergonomic design, personalized design, or virtual try-on. A digital human body is normally represented in a high-dimensional space, and the number of vertices in a mesh is far larger than the number of human bodies in publicly available databases, which results in a model learnt by Principle Component Analysis (PCA) can hardly reflect the true variety in human body shapes. Furthermore, if the number of vertices and size of database are large, it will be very challenging to perform PCA on such a huge problem. This paper presents a hierarchical method for statistical learning of human body by using feature wireframe as one of the layers to separate the whole problem into smaller and more solvable sub-problems. The feature wireframe is a collection of feature curves which are semantically defined on the mesh of human body, and it is consistent to all human bodies. A set of patches can then be generated by clustering the whole mesh surface to separated ones that interpolate the feature wireframe. Since the surface is separated into patches, PCA only needs to be conducted on each patch but not on the whole surface. The spatial relationship between the patches and the wireframe are learnt by linear regression. An application of semantic parametric design is used to demonstrate the capability of the method, where the semantic parameters are linked to the feature wireframe instead of the mesh directly. Under this hierarchy, the feature wireframe acts like an agent between semantic parameters and the mesh, and also contains semantic meaning by itself. The proposed method of learning human body statistically with the help of feature wireframe is scalable and has a better quality.

Details:

Full paper: 17.337.pdf
Proceedings: 3DBODY.TECH 2017, 11-12 Oct. 2017, Montreal QC, Canada
Pages: 337-346
DOI: 10.15221/17.337

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